Method and apparatus for determining markers of health by analysis of blood

ABSTRACT

Biomarkers of high blood pressure are measured to identify high blood pressure of the subject based on one or more biomarkers. In many embodiments, the response of the biomarker to blood pressure occurs over the course of at least an hour, such that the high blood pressure identification is based on a cumulative effect of physiology of the subject over a period of time. The methods and apparatus of identifying high blood pressure with biomarkers have the advantage of providing improved treatment of the subject, as the identified biomarker can be related to an effect of the high blood pressure on the subject, such as a biomarker corresponding to central blood pressure. The sample can be subjected to increases in one or more of pressure or temperatures, and changes in the blood sample measured over time.

CROSS-REFERENCE

This application is a continuation of U.S. application Ser. No.15/783,328, filed Oct. 13, 2017, entitled “METHOD AND APPARATUS FORDETERMINING MARKERS OF HEALTH BY ANALYSIS OF BLOOD” which is a bypasscontinuation of PCT Application Serial No. PCT/US2016/026825, filed Apr.8, 2016, entitled “METHOD AND APPARATUS FOR DETERMINING MARKERS OFHEALTH BY ANALYSIS OF BLOOD” which claims priority to U.S. ProvisionalApplication Ser. No. 62/147,507, filed Apr. 14, 2015, entitled “METHODSAND APPARATUS FOR DETERMINING MARKERS OF HEALTH BY ANALYSIS OF BLOOD”,and U.S. Provisional Application Ser. No. 62/213,234, filed Sep. 2,2015, entitled “METHODS AND APPARATUS FOR DETERMINING MARKERS OF HEALTHBY ANALYSIS OF BLOOD”, the entire disclosures of which are incorporatedherein by reference in their entireties for all purposes.

BACKGROUND

The field of the present invention is related to biomarkers of health,and more specifically to one or more of detecting, diagnosing,screening, tracking over time, or ruling out, one or more conditionssuch as high blood pressure and the harmful cardiovascular effects ofhigh blood pressure. Examples of harmful effects of high blood pressurecan include one or more of inflammation, coronary artery disease, stableplaques, unstable plaques, or other vascular factors related to theonset of heart disease and heart attack in humans.

Prior methods and apparatus of measuring biomarkers are less than idealin at least some respects. Prior methods and apparatus of measuringblood pressure and diagnosing subjects can be less than ideal in atleast some instances. Although blood pressure measurements can be usedto assess the health of a subject and guide treatment, the prior methodsand apparatus can be less than ideal. Work in relation to embodiments asdescribed herein suggest that the prior peripheral blood pressuremeasurements can be less than ideally suited to guide therapy of atarget tissue. For example, some organs such as the heart receive bloodfrom the central vasculature and the prior peripheral blood pressuremeasurements may be less than ideally suited to guide therapy to suchorgans. Also, pressure measurements may be less than ideally suited toguide at least some treatments having a physiological effect on thesubject's health, and measuring blood pressure is a somewhat indirectway of measuring subject physiology and characteristics that may berelated to the tissues and blood of subject.

Modern blood pressure measurements are based on the sphygmomanometer,also referred to as a blood pressure cuff. The sphygmomanometer wasinvented by Samuel Siegfried Karl Ritter von Basch in 1881. Asphygmomanometer in the form of a cuff was patented in 1955 (GB740181).Although the sphygmomanometer remains a very important tool in medicine,it can have problems and deficiencies in at least some instances.

The sphygmomanometer in combination with a stethoscope allows a trainedhealth professional to measure two characteristic values related toblood dynamics, the systolic and the diastolic pressure. The health carepractitioner attaches the cuff around the subject upper arm over thebrachial artery. Practitioner pumps up pressure in the cuff until thebrachial artery is completely occluded. While listening to the brachialartery at the inside crease of the elbow, practitioner slowly releasespressure in the cuff. As the pressure falls, a whooshing sound is heard.These so-called Korotkoff sounds occur when blood flow first startsagain in the artery. The pressure at which this sound is first heard isnoted as the systolic blood pressure. The cuff pressure is releasedfurther until the Korotkoff sounds can no longer be heard. This is notedas the diastolic blood pressure. The peak pressure in the arteries isthe systolic pressure, and the lowest pressure (at the resting phase ofthe cardiac cycle) is the diastolic pressure. The systolic and diastolicpressure measurements have become the medical standard of care fordiagnosing high blood pressure.

Although helpful in diagnosing high blood pressure, the systolic anddiastolic blood pressure measurements can result in less than idealmeasurements that may be related to one or more of the following:

Observer error;

Systematic intraobserver and interobserver errors;

Terminal digit preference, rounding to favorite digit;

Observer prejudice;

White coat hypertension—high only in doctor's office;

Masked hypertension—normal in office, high at other times of day;

Instrument error;

Defective control valve;

Improper fit of cuff, too large or too small;

Inadequate length of tubing;

Connections not airtight;

Position of manometer causes reading error;

Placement of cuff error;

Diastolic dilemma—muffling of sounds can occur 10 mm before completedisappearance;

Two arms can exhibit different readings; or

Deflation too rapid.

These errors can lead to inaccurate blood pressure readings that may berelated to improper diagnoses in at least some instances. For example,errors as large as 20 mm Hg may occur in at least some instances.

If a subject is incorrectly diagnosed as having high blood pressure whenactually having low blood pressure, this person may be placed on a dailyblood pressure medication. Many of these medications may have sideeffects, and more people than would be ideal can be subjected to theside effects of blood pressure medications. Also, blood pressuremeasurement errors may result in a person who actually has high bloodpressure being misdiagnosed as having low blood pressure. An incorrectdiagnosis for a subject with high blood pressure can result in thatsubject not receiving appropriate medication, such that the high bloodpressure may not be untreated in at least some instances. Inappropriatemanagement of high blood pressure can result in injury to the subjectand may even be fatal in at least some instances, and it would behelpful to have fewer misdiagnoses of high blood pressure.

Blood pressure measurements located at the brachial artery may be lessthan ideally suited to guide treatment. For example, the brachial arteryis located away from the aorta other central blood vessels and providesa less than ideal determination of central blood pressure, and measuringsystolic and diastolic pressure in the brachial artery of the arm may beless than ideally suited to diagnose central high blood pressure thatcan be related to organ damage in at least some instances. Although betablocker medications can lower peripheral blood pressure and bloodpressure of the arteries in the arm, these medications may not lowercentral blood pressure in at least some instances, and people treatedwith beta blockers having normal brachial pressure may still experienceheart failure.

Work in relation to embodiments suggest that it would desirable to havea record of blood pressure and of cardiovascular health over a period oftime, rather than an instantaneous measurement like brachial cuffpressure.

Although blood chemistry is the gold standard for screening, diagnosis,and therapy in health wellness and medicine, the prior methods are lessthan ideal in at least some respects. Currently, a blood panel isrequested by a physician and the patient is instructed to travel to ablood laboratory where a phlebotomist can draw blood from theantecubital vein into a series of special collection tubes. The blood isthen sent to a central blood chemistry laboratory where it is chemicallyanalyzed using numerous wet chemical assays that have been developed andvalidated over the years. More recently, a small portion of these testscan be performed in a physician's office using specialized machinesemploying enzymatic assays. Such delivery of blood to various locationscan be less than ideal.

Blood chemistry testing is rapidly moving to the point-of-care for manyreasons. The biggest of these are cost and compliance. Blood testing inthe POC and eventually in the home dives down healthcare costs, istrackable and reportable, is immediate and actionable, sticky, andsocially supportive compared to central lab testing. But the problemthat needs to be overcome is that central lab methods generally do nottranslate to the POC and the home, since they require much wet chemistryand expensive instrumentation.

Measurement and detection of biomarkers can be done in conjunction withmodern computers and software. These prior computers and software canless than ideally solve the technical problem of the detection andidentification of biomarkers related health of a subject. The priorsoftware and algorithms can be less than ideally suited to determine thehealth of a subject in response to data such as spectral data.

In light of the above, it would be desirable to provide improved methodsand apparatus for measuring biomarkers of a patient, such as biomarkersuseful in determining blood pressure. Ideally such methods and apparatuswould provide a more accurate reading of blood pressure with lessvariability and fewer false negatives and false positives for high bloodpressure, provide a more accurate determination of central bloodpressure, allow improved treatment and management of blood pressure, andprovide an indicator of blood pressure and cardiovascular health overtime.

SUMMARY

Embodiments are directed to measurement of samples in order to determineone or more biomarkers related to health. In many embodiments, the oneor more biomarkers comprises a biomarker of a cell membrane, such as abiomarker of a red blood cell membrane. The biomarker may comprise oneor more of a component of a cell membrane, or a substance such as amolecule that interacts with the membrane.

Embodiments can provide improved methods and apparatus of identifyinghigh blood pressure of a subject. In many embodiments, one or morebiomarkers of high blood pressure are measured in order to identify highblood pressure of the subject. Identifying the blood pressure of asubject based on one or more biomarkers has the advantage of being moreaccurate and less susceptible to short term fluctuations in physiologyand user variability at the time of the measurement. In manyembodiments, the response of the biomarker to blood pressure occurs overthe course of at least an hour, for example at least a day, such thatthe high blood pressure identification is based on a cumulative effectof physiology of the subject over a period of time such as an hour, aday or weeks, as opposed to the very short amount of time during which ablood pressure measurement is made at a clinic and can fluctuate. Themethods and apparatus of identifying high blood pressure with biomarkersas disclosed herein have the advantage of providing improved treatmentof the subject, as the identified biomarker can be related to an effectof the high blood pressure on the subject, such as a biomarkercorresponding to central blood pressure. The sample can be subjected toincreases in one or more of pressure or temperatures, and changes in theblood sample measured over time.

In many embodiments, the apparatus comprises a first measurement channelto measure the blood sample near a measurement surface with anevanescent wave of an internally reflected light beam, and a secondmeasurement channel to measure the blood sample through a thickness ofthe sample with a transmission measurement. The transmission measurementcan be measured through the measurement surface and the thickness of thesample, such that an internally reflected measurement beam and atransmission measurement beam overlap at least partially. In manyembodiments, the evanescent wave measurement comprises an evanescentwave spectroscopy measurement and the transmission measurement comprisesa transmission spectroscopy measurement. While the measurement surfaceand first channel and the second channel can be configured in many ways,in many embodiments the measurement surface comprises a measurementsurface of a Dove prism and the internally reflected measurement beam istransmitted through inclined surfaces on opposing ends of the Doveprism.

In many embodiments, the blood sample comprises a first component havingred blood cells or clotted cells and a second component comprisingplasma or serum and each of the first component and the second componentis measured. Each of the components can be measured with the evanescentwave spectroscopy and the transmission spectroscopy in order to providefour measurement channels.

In a first aspect, embodiments provide an apparatus to identify highblood pressure of a subject. The apparatus comprises a processorcomprising instructions to identify a blood pressure biomarker of ablood sample of the subject.

In another aspect, embodiments provide a method of identifying highblood pressure of a subject. A blood pressure biomarker of a bloodsample of the subject is identified.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the features and advantages of the presentdisclosure will be obtained by reference to the following detaileddescription that sets forth illustrative embodiments, in which theprinciples of the disclosure are utilized, and the accompanying drawingsof which:

FIG. 1 shows a blood sample from a subject being placed on a measurementsurface in order to measure blood pressure biomarkers, in accordancewith embodiments;

FIG. 2 shows a side profile view and corresponding dimensions of a redblood cell, in accordance with embodiments;

FIG. 3 shows measurement of a blood sample with a Dove prism in order toidentify high blood pressure biomarkers with a first measurement channeland a second measurement channel, in accordance with embodiments;

FIG. 4 shows red blood cells located on a measurement surface to measurethe red blood cells with an evanescent wave and identify high bloodpressure biomarkers of the red blood cell membranes, in accordance withembodiments;

FIG. 5 shows an apparatus to measure blood pressure biomarkers, inaccordance with embodiments;

FIG. 6 shows a method of measuring blood pressure biomarkers, inaccordance with embodiments;

FIG. 7 shows a substantially circular cross-section through a red bloodcell, in accordance with embodiments;

FIG. 8 shows measurement of a red blood cell membrane and relatedstructures, in accordance with embodiments;

FIG. 9 shows an apparatus comprising a database and a user interface todetermine identify markers of red blood cells related to health, inaccordance with embodiments;

FIG. 10 shows light entering germanium (index of refraction n=4) at anincident angle of 80 degrees, resulting in total internal reflection anda very shallow 1/e penetration depth of the resulting evanescent waveinto the sample, in accordance with embodiments;

FIG. 11A shows a sample gravimetric washing container and spectrometerto measure a blood sample, in accordance with embodiments;

FIG. 11B shows a container as in FIG. 11A removed from the spectrometer;

FIG. 11C shows a draw tube, in accordance with embodiments;

FIG. 11D shows sample delivery and cell washing, in accordance withembodiments;

FIG. 12 shows a method of analyzing a sample, in accordance withembodiments;

FIG. 13 shows a commercially available spectroscopy apparatus suitablefor combination, in accordance with embodiments;

FIG. 14 shows example spectra of fat, milk, dried red blood cells, redblood cells, red meat and red wine, in accordance with embodiments;

FIG. 15 shows PCA analysis of blood samples with and without aspirin, inaccordance with embodiments;

FIG. 16A shows multivariate curve resolution (MCR) factors of an aspirinstudy, in accordance with embodiments;

FIG. 16B shows MCR concentrations for the factors of FIG. 16A, inaccordance with embodiments;

FIG. 17 shows a comparison between fresh and gluteraldehyde-stiffenedchicken red blood cells (measurement time of one minute), in accordancewith embodiments;

FIG. 18 shows the effect of aspirin on the red blood cell membrane, inaccordance with embodiments;

FIG. 19 shows shifts in factor 3, factor 6, and factor 10, in accordancewith embodiments;

FIG. 20 shows a 3D plot of spectral data normalized to the Amide I peakfor blood before and after gluteraldehyde addition, in accordance withembodiments; and

FIG. 21 shows a 2D plot of the spectral data of FIG. 20;

FIG. 22 shows a method of spectral data analysis suitable forincorporation with embodiments;

FIG. 23 shows results from a study of mean arterial blood pressuremeasurements in human subjects using a sphygmomanometer or bloodpressure cuff;

FIGS. 24A and 24B show results from a study of average blood pressuremeasurements in human subjects, using a measurement apparatus inaccordance with embodiments;

FIGS. 25A and 25B show additional results from the study of FIGS. 24Aand 24B;

FIG. 26 shows the pure component spectrum of red blood cells obtainedfrom spectroscopic data generated from the study of FIGS. 23-25B;

FIG. 27 illustrates a method of applying a genetic algorithm to select asubset of wavelengths;

FIG. 28 is a schematic illustration of the step of generating initialwavelength strings;

FIG. 29 shows a method of calculating the fitness of a calibration modelgenerated using selected wavelengths;

FIG. 30 is a schematic illustration of the step of performing crossoverof wavelength strings;

FIG. 31 is a schematic illustration of the step of performing mutationof wavelength strings;

FIG. 32 illustrates a method of applying a genetic algorithm to select asubset of blood pressure reference values;

FIG. 33 illustrates a method of applying a genetic algorithm to select asubset of sample drying time points;

FIG. 34 shows the results of a genetic algorithm optimization ofwavelength selection for blood spectroscopic analysis;

FIG. 35 shows the final selection of wavelengths of a red blood cellspectrum, optimized using the genetic algorithm procedure of FIG. 35;and

FIG. 36 shows the predicted systolic blood pressure (SP) values (mmHg)derived from spectroscopic measurements of blood samples from the studyof FIGS. 24A and 24B, wherein wavelengths selected using a geneticalgorithm were used to generate the predicted values.

DETAILED DESCRIPTION

A better understanding of the features and advantages of the presentdisclosure will be obtained by reference to the following detaileddescription that sets forth illustrative embodiments, in which theprinciples of embodiments of the present disclosure are utilized, andthe accompanying drawings.

Although the detailed description contains many specifics, these shouldnot be construed as limiting the scope of the disclosure but merely asillustrating different examples and aspects of the present disclosure.It should be appreciated that the scope of the disclosure includes otherembodiments not discussed in detail above. Various other modifications,changes and variations which will be apparent to those skilled in theart may be made in the arrangement, operation and details of the methodand apparatus of the present disclosure provided herein withoutdeparting from the spirit and scope of the invention as describedherein.

The embodiments disclosed herein can be combined in one or more of manyways to provide improved measurements of blood samples from a subject.

As used herein like characters identify like elements.

In many embodiments, an evanescent wave comprises a near-field wave withan intensity having an exponential decay as a function of the distancefrom the boundary at which the wave was formed. Materials place on asurface can interact with the near field wave, with or withoutabsorption, for example. This use of the evanescent near field wave canprovide improved signal to noise ratios when measuring the membrane ofcells such as the red blood cell. The localization of the evanescentwave intensity profile to the cell membrane can provide an effectiveamplification of the measured signal.

The shape of the red blood cell (hereinafter “RBC”) is particularly wellsuited for evanescent wave measurement as disclosed herein. The redblood cell membrane comprises a biconcave disk shape having a flattenedregion along the long dimension and an indentation near center, whichallows the red blood cells to settle onto a measurement substrate suchthat the long dimension of the red blood cell extends in a directionalong the surface of the substrate, such that a significant portion ofthe red blood cell membrane along the long dimension can be exposed tothe evanescent wave and measured. The red blood cell membrane comprisesproteins and lipids, and this structure provides properties forphysiological cell function such as deformability and stability.Approximately 2.4 million new erythrocytes are produced per second. Thecells develop in the bone marrow and circulate for about 100-120 days inthe body before their components are recycled by macrophages. Thedeformability of the human red blood cell results from the dynamicinteraction of the phospholipid bilayer plasma membrane and thestructural spectrin molecular network. Adenosine 5′-triphosphate (ATP)facilitates remodeling in the coupled lipid and spectrin membranes.

As used herein, a red blood cell encompasses an erythrocyte.

The embodiments disclosed herein can be combined in one or more of manyways.

In many embodiments, the detection and diagnosis of disease and wellnessthrough reagent-less whole cell in vitro analysis of changes in theerythrocyte membrane from a single drop of blood collect via a lancingdevice is provided.

The embodiments as disclosed herein are particularly well suited forperforming spectroscopic analysis of RBC proteins, lipids, andcombinations thereof, for example for assessing the risk ofcardiovascular diseases. The spectroscopic analysis can be performedwithout in vitro enzymatic analysis, and without lysing the cells orpretreating samples, for example.

In many embodiments, spectroscopic analysis of the RBC for detectingcell stress and changes in cell morphology associated with hypertensionallows retroactive assessment of past cell damage due to elevated bloodpressure. The retroactive assessment can significantly decrease the needfor continuous blood pressure measurement, and in many embodiments caneliminate bias due to patient's mood or emotional state. The membrane ofthe erythrocyte undergoes molecular changes, or remodeling, inhypertension. These changes appear to be a response to increased shearforces on the cells as blood pressure increases. When erythrocytesundergo shear stress in constricted vessels, they can release ATP, whichcauses the vessel walls to relax and dilate so as to promote normalblood flow.

In many embodiments, the RBC is used as messenger cell to report diseasemarkers which the RBC encounters during circulation.

The apparatus embodiments as disclosed herein are particularly wellsuited for performing analysis of red blood cells as disclosed herein.

In many embodiments, the apparatus comprises a user interface and one ormore databases for performing one or more of the analyses as disclosedherein.

In many embodiments, the red blood cells (erythrocytes) are separated,for example with standard method, such as centrifuge. Alternatively orin combination, whole blood is separated gravimetrically such that therelatively heavier erythrocytes fall onto the sampling surface asdescribed herein.

While the analytical method and apparatus can be configured andperformed in many ways, in many embodiments, the methods and apparatusare configured for one or more of measurement of mechanical or molecularproperties via infrared, near-infrared, UV, Raman, Surface enhancedRaman, resonance Raman, fluorescence, NMR, terahertz, far infrared,circular dichroism) or through a mechanical test (mechanical stiffness),or through a thermal property analysis (thermal gravimetric analysisTGA). In many embodiments, the analytical methods and apparatus comprisemolecular spectroscopy methods and apparatus, such as one or more ofinfrared, Raman or near-infrared spectroscopy, for example. The methodsand apparatus can be configured to perform one or more of measurementsin transmission, absorbance, photo acoustic, or reflection mode, ininternal reflection mode, for example.

In many embodiments, the erythrocyte membrane is measured for changes.The erythrocyte membrane can undergo molecular changes during one ormore of many disease states. Examples of examples of membrane changesrelated to disease states that can be measured in accordance withembodiments include:

Average blood glucose (membrane protein glycosylation)

High blood pressure (membrane elasticity)

Inflammation (fibrinogen on surface of membrane)

Cerebrovascular disorders (fibrinogen binding on RBC membrane)

Thrombosis (erythrocyte agglomeration)

Unstable plaque (lipid on surface of cell membrane)

Acetylsalicylic Acid (ASA) therapy (cell membrane ⋅slippery-ness⋅)

Malaria (cell deformation)

Dehydration (membrane water content)

Sepsis (erythrocyte sedimentation rate)

Blood bank aging

Myocardial infarction (rigidity)

Diabetes (rigidity)

Sickle cell anemia (deformation)

Malaria (deformation, lipid profile)

Exercise oxidative stress (loss of C═C bonds)

Antioxidant level (ceruloplasmin level)

Drug uptake (Codeine, chlorpromazine, imipramine, mefloquine, andpyrimethamine, acetazolamide, methazolamide, and chlorthalidone and theocular pressure reducing agent, dorzolamide)

Hemolytic Anemia (lipid ratios)

Preeclampsia (membrane rigidity)

Ionic balance (protein stricture)

pH (protein structure)

Alzheimers (AD) (levels of proteins in membrane skeleton)

Malnutrition (kwashiorkor and marasmus) (elevatedCholesterol/phospholipid ratio)

Hereditary Spherocytosis (deficiency of ankyrin, spectrin and protein4.2)

Hereditary Elliptocytosis (spectrin defects, glycophorin deficiency)

Acanthocytosis (free cholesterol/phospholipid ratio)

Alcohol (association with lipid bilayer)

Coumadin therapy dosimetry

Whole blood viscosity

In many embodiments, the presence of undesirable effects of high bloodpressure on the vascular system can be identified in one or more of manyways. In many embodiments, an amount of one or more biomarkers of theblood can measured in order to identify high blood pressure of thesubject. For example, a level of biomarker in the blood can provide anindication of high blood pressure, and in many embodiments an amount ofbiomarker from a blood sample above a threshold amount can identify thesubject as having high blood pressure. In many embodiments, the methodsand apparatus to measure the biomarker can provide an improvedidentification of blood pressure with fewer false positives and falsenegatives than at least some prior cuff measurements of the brachialartery, for example.

Work in relation to embodiments as described herein suggests that thered blood cells (hereinafter “RBCs”) can be involved in the signaling ofhigh blood pressure, and the methods and apparatus as described hereincan measure one or more RBC markers related to the RBC signaling of highblood pressure. For example, increased mechanical pressure on the RBCscan induce the RBCs to release one or more biomarkers such ATP, forexample. The released ATP may signal changes to the blood vessel walls,or transmit signals to the blood vessel walls, or both, for example.Alternatively or in combination, cell membranes of the RBCs may stiffen,thereby indicating chemical changes in the cell membrane of the RBC.Although these effects may not yet be fully understood, the RBCsignaling, reporting, and responding to high blood pressure can becombined with measurements of the RBCs to identify high blood pressureof the subject, in accordance with many embodiments as described herein.

In many embodiments disclosed herein, a biomarker provides a record ofblood pressure and of cardiovascular health over a period of time,rather than an instantaneous measurement like brachial cuff pressure. Inmany embodiments, the metric or biomarker is related to recent historyof high blood pressure would be. For example, a time period of 90-120days can be particularly useful for reasons similar to that HemoglobinA1c marker is useful for controlling blood sugar in diabetes. Such amarker can be especially useful for providing health and lifestyleadvice to a patient. Such a marker can also be especially useful forensuring the proper dosage and efficacy of a drug used to treat highblood pressure, and for determining compliance with taking a therapeuticagent, in accordance with embodiments disclosed herein.

In many embodiments, RBCs are large and as they travel through thevasculature, can come in contact with vessel walls that leave chemicalresidue on the RBCs, for example. In this manner the RBC membranecomprises markers to identify and determine the chemistry of the liningof the vessels walls. In many embodiments, when this transfer occurs,the RBCs comprise a marker of the atherosclerotic plaque that can beused to report the presence of atherosclerotic plaques within the bloodvessels. The corresponding chemical spectrum obtained from the RBCs canbe used to differentiate the presence of an unstable plaque from astable plaque, which spectra are chemically distinct, for example.

In many embodiments, atherosclerotic plaques comprise one or more ofthree categories: foam-cell rich, lipid-rich, or collagen-rich. In manyembodiments, the distinct chemistry of each plaque leaves distinctresidue patterns on the outside of the red blood cell membrane.Lipid-rich plaques have been associated with dangerous unstable plaques.The residual material of the one or more plaques can be deposited on thered blood cell membrane and measured in accordance with embodimentsdescribed herein.

In some embodiments, a substance is injected into the blood. Thesubstance may comprise one or more of mild abrasive, stickiness, oraffinity to atherosclerotic plaques, for example. The affinity can bespecific to one or more of the plaques as described herein. After aperiod of time, this substance can be recovered from blood via a blooddraw. By measuring the exterior of these substance particles, thepresence of unstable plaques can be detected. The substance may compriseone or more of many known substances such as one or more of many knownsugars, for example.

In many embodiments, the abrasive substance comprises a non-toxicmaterial that causes alteration to the blood to in order to cause aheightened but temporary level of abrasion and inflammation in thecoronary arteries. In many embodiments, the substance clears from theblood in a short time after the measurement is made. An example of asuitable candidate substance is a sugar, such as one or more of glucose,fructose, or mannose, for example. High blood sugar can be a knowncondition in diabetes. Although sugar is known to cause inflammation inthe vasculature and can increase agglomeration in red blood cells, sugarclears naturally from the blood system, since it is metabolized readily.

In many subjects, the lifetime of an RBC can be approximately 90 to 120days. The changes of the RBC due to high blood pressure can be relatedto the relatively recent history of high blood pressure over the courseof the lifetime of the RBC. If a medication is taken for high bloodpressure, the characteristics of the RBCs can revert to normalrelatively quickly because of the rapid turnover of these cells, forexample. Alternatively or in combination, an amount of the one or moresignaling biomarkers stored on or within the RBC, such as ATP, can berelated to blood pressure of the subject, for example related to shearstress of the RBC during cardiac cycling of the RBC.

There may be changes in other blood constituents as well. For example,stiffened RBCs can be somewhat abrasive in the vessels, which can leadto inflammation and additional biomarkers suitable for measurement inaccordance with embodiments disclosed herein. While many biomarkers canbe measured in accordance with embodiments disclosed herein, an exampleof such biomarker suitable for measurement is C-reactive protein(hereinafter “CRP”), for example.

Proteins in blood can also change conformation in response to pressure.Proteins such as albumin which exists in high concentration in blood mayalso be measured in order to identify high blood pressure of a subject.

In many embodiments, one or more components of blood are analyzed suchas the serum component of blood, or the cellular component of blood, orboth, in order to determine the presence of biomarkers of high bloodpressure.

In many embodiments, and amount of blood such as a drop of blood isprovided for analysis. For example, an amount of blood can be providedinto a capillary tube which has been heparinized. The RBCs can be causedto separate from the serum. An instrument configured in accordance withembodiments as described herein can pass a beam of light may through thecapillary tube, to measure one or more of the serum portion, or thecellular portion, or both, for example. The capillary tube can bepressurized, and one or more of the constituents in blood such asproteins may respond differently to pressure when the blood has beensubjected to high blood pressure, such that a differential measurementobtained. For example, a first measurement can be obtained at firstpressure and a second measurement obtained a second higher pressurehigher than the first pressure. For example, the first pressure can beapproximately atmospheric pressure, and the second pressure can begreater than atmospheric pressure. In many embodiments, pressures ashigh as 600 MPa can be used to cause the unraveling and denaturation ofproteins in the blood. The rates and dynamics of these protein changesin response to applied external pressure can be correlated with theblood having been subjected to high blood pressure previously within thesubject.

FIG. 1 shows a blood sample 30 from a subject being placed on ameasurement surface 100 in order to measure blood pressure biomarkers.The blood sample is obtained from the subject. The subject has a hand 10from which a blood sample can be obtained, for example. Although a handis shown the blood sample can be obtained in one or more of many knownways. The blood sample is placed on a measurement surface.

In many embodiments, the measurement surface on which the red bloodcells 40 are placed comprises an optical prism 110 for the purpose ofchanneling measurement light 115 under the blood, through the prism, byinternal reflection. Internal reflection spectroscopy can makespectroscopic measurements at a shallow depth beyond the prism surface,since an evanescent wave is set up at that interface. This rapidlydiminishing evanescent wave rapidly diminishes with distance away fromthe prism surface. The resulting spectrum is thereby resulting from onlythe material that is resting closest to the prism. In our blood cellsample, the spectrum would contain information mainly about the cellmembrane and not the cytoplasm. One proposed mechanism of action forcorrelating with blood pressure is changes in the cell membrane of thered blood cells as a biomarker. In many embodiments, the membranespectrum contains spectra of one or more biomarkers having amountscorresponding to the blood pressure of the subject.

The measurement surface can be configured in one or more of many ways tomeasure the sample. In many embodiments, the measurement surfacecomprises a flat surface of an optically transmissive material such asSilicon or Germanium, for example. The optically transmissive materialcan be shaped in one or more of many ways to provide the measurementsurface as described herein. For example, the optically transmissivematerial may comprise a prism, a flat plate, a cube, a rectangle or aDove prism, for example.

In many embodiments, the sample is measured near the measurement surfacewith total internal reflection spectroscopy (hereinafter “TIR”). WithTIR, the measurement light beam is directed toward the surface at anangle so as to provide total internal reflection of the light beam fromthe measurement surface. Although the light beam is reflected internallyfrom the surface, the light beam can interact with the sample on theopposite side of the surface from the light beam with an evanescent waveof the light beam. The evanescent wave of the light beam extends beyondthe measurement surface by a distance related to the wavelength of themeasurement light beam. In many embodiments, the evanescent wave extendsbeyond the surface so as to provide a penetration depth of about 0.1λinto the sample place on the measurement surface, where λ is thewavelength of light. The TIR light may comprise one or more of visiblelight, near-infrared light, mid-infrared light or far infrared light,for example. In many embodiments, the light used comprises mid-infraredlight having one or more wavelengths within a range from about 2 μm(micrometer) to about 20 μm, for example. The one or more wavelengths oflight may comprise a plurality of wavelengths of light to scan to aplurality of depths of the sample.

With TIR spectroscopy, the depth of the measurement is related to themeasurement wavelength such that the membranes of red blood cells on ornear the surface can be measured. With a 2 μm wavelength, thepenetration depth is about 0.2 μm such the penetration depth of the TIRmeasurement does not extend beyond a thickness of a red blood cell. Witha 20 μm wavelength, the penetration depth is about 2 μm such thepenetration depth of the TIR measurement corresponds to the approximatea thickness of a red blood cell.

FIG. 2 shows a side profile view and corresponding dimensions of a redblood cell 40. The red blood cell comprises an approximately toroidalshape having a long dimension along an elongate axis defining a length42 of the red blood cell and a short dimension along a transverse axisdefining a thickness 44 of the red blood cell. The length of the redblood cell is approximately 7 (seven) microns and the width isapproximately 2 (two) microns.

When the red blood cell is forced through an opening with blood pressuresuch as an opening of a capillary channel sized smaller than the redblood cell, the shape of the red blood cell can change to allow the redblood cell to pass, and one or more biomarkers such as ATP can bereleased. Alternatively or in combination, high central blood pressurecan result in one or more of deformation of the red blood cell orsurface changes to the red blood cell related to the high central bloodpressure of the subject, and the biomarkers corresponding to thesechanges can be measured in accordance with embodiments disclosed herein.

In many embodiments, the methods and apparatus are configured to measurethe surface of the red blood cells and identify one or more componentsof the red blood cells specifically. A sampling and measurement systemcan be configured to first separate cells from serum or plasma throughsedimentation, then place a sample of blood cells onto one measuringstage and a sample of serum onto another measuring stage, for example,so as to provide separate measurements. The volume of blood sample canbe small, such as a drop that could be obtained by a lancet at a finger.The stage holding the blood cells may comprise a horizontal surface onwhich the blood cells can be placed as described herein. The measuringstage holding the serum or plasma may comprise another measuring surfacefor TIR or transmission measurements as described herein, andcombinations thereof, for example.

FIG. 3 shows measurement of a blood sample 30 with a Dove prism 300 inorder to identify high blood pressure biomarkers with a firstmeasurement channel and a second measurement channel. In manyembodiments, the first measurement channel comprises a TIR measurementchannel, and the second measurement channel comprises an opticaltransmission channel extending through a thickness of the sample. TheDove prism can provide a first inclined surface 305 and a secondinclined surface 310 that allow the first measurement light beam 315 tobe totally internally reflected and directed to the inclined surfaces atan angle that decreases reflection from the inclined surfaces. The Doveprism, like many shapes, comprises a surface 320 opposite the TIRmeasurement surface 100 that receives a second measurement beam 325 fortransmission through the measurement surface and bulk of the sample. TheDove prism comprises an elongate axis 330 extending axially through theinclined surfaces and between the measurement surface and the opposingsurface.

In many embodiments, a transparent movable support 350 is provided toshape an upper surface of the sample for transmission of the secondmeasurement light beam. The transparent movable support may comprise athickness suitable for pressurizing the sample with a pressure surface355 for measurements as described herein. Alternatively, the transparentmovable support can be thin to shape the blood sample withoutpressurizing the blood sample, for example a microscope slide.

Although a Dove prism is shown, the optical system can be configured inone or more of many ways with one or more of prisms, cubes, rhomboids orparallelepipeds, for example.

FIG. 4 shows red blood cells 40 located on a measurement surface 100 tomeasure the red blood cells with an evanescent wave generated from thetotal internal reflection of the measurement light beam 115 in order toidentify high blood pressure biomarkers of the red blood cell membranes46, in accordance with embodiments.

The blood sample 30 can be prepared in one or more of many ways forplacement on the measurement surface. In some embodiments, themeasurement surface or a solution combined with the blood samplecomprises a clotting antagonist to inhibit blood clotting, in order toallow measurement of red blood cells and to separate the blood cellsinto a first component having a greater number of red blood cells and asecond component having a greater amount of plasma as compared to thesample as drawn from the subject. Alternatively, the blood sample can beallowed to clot such that the sample comprises a first clot componentand a second serum component, in which the clotting factors of theplasma have been substantially depleted to form the blood clot.

In many embodiments, the components of the serum 32 or plasma 34 and theblood cells 40 are each measured. In many embodiments, the plasma andblood cells can be separated at least partially so as to providedifferent measurements for each, for example separate simultaneousmeasurements of each.

In many embodiments, a second beam of light can be transmitted throughthe blood sample. In these embodiments, a spectrum representative of thebulk of the measurement cell is obtained. The second stage can be asimilar internal reflection prism to measure the blood serum both byinternal reflection and by transmission. The transmission measurementrepresents the bulk of the serum or plasma. In many embodiments, theproteins 36 in the blood can begin to coat the prism as time progresses.Therefore the internal reflection channel becomes a way of measuring theproteins in blood with greater intensity than could be measured in thebulk serum sample. Alternatively or in combination, the red blood cellscan sediment downward onto the measurement surface, and the membranes ofthe red blood cells within the penetration depth of the evanescent wavecan be measured and the bulk of the plasma measured with thetransmission beam.

In many embodiments, two measurement cells on two measurement stages canbe used to measure the two components of blood separately such that fourmeasurements from four independent measurement channels are provided.The evanescent wave measurements can be combined with the transmissionmeasurements so as to provide four different spectral channels. Each ofthese channels can be interrogated with different wavelengths of light,from the visible to the far infrared region.

In many embodiments, each of these channels is measured as a function oftime to follow changes in the blood cells and the serum and/or plasmawith time. During this time, the samples can be subjected to differenttemperatures by embedding a heating or cooling element into the stages.Alternatively or in combination, a movable transparent support 350comprising an optical window can be added on top of the blood cell andserum or plasma sample. This support comprising the window can bemounted in a frame which can create a pressure seal at the stage. Inmany embodiments, a high external pressure can be exerted on the bloodcells and blood serum. Pressures of up to 600 MPa can be used in orderto denature and change the structure of the components and specificallyproteins in the sample, for example. In many embodiments, these dynamicmeasurements can identify differences among biomarkers in blood that hasbeen exposed to high blood pressure versus blood from subject withouthigh blood pressure, for example.

FIG. 5 shows an apparatus 500 to measure blood pressure biomarkers. Theapparatus comprises a first measurement stage 505 comprising a surface100 to receive a blood sample 30 of a subject. In many embodiments, theapparatus comprises a second stage 510 to receive a second sample of thesubject as described herein. For example, the first sample may comprisea red blood cell component and the second sample may comprise a plasmacomponent, in which the red blood cell component comprises a greateramount of red blood cells than the initial sample from the subject andthe plasma component comprises a greater amount of plasma than theinitial sample from the subject, for example. The first measurementstage and the second measurement stage may comprise similar componentsand can be coupled to light sources, optics and detectors similarly andin accordance with embodiments as described herein.

The apparatus to identify blood pressure biomarkers comprises one ormore light sources, for example first light source 515 and second lightsource 520. The apparatus comprises one or more input optics opticallycoupled to the light sources so as to receive light from the lightsources, for example first input optics 525 for TIR measurements andsecond input optics 530 for bulk transmission measurements. Theapparatus comprises one or more output optics optically coupled to thesample container to receive the light from the sample, for example firstoutput optics 535 to receive the TIR light and second output optics 540to receive the transmission light. The one or more output optics areoptically coupled to one or more detectors, for example first detector545 coupled to output optics 535 and second detector 550 coupled tooutput optics 540.

The components of the apparatus 500 can be coupled to a processor 555comprising instructions to control the measurement of the sample, forexample of the first sample stage. In many embodiments, the processor isconfigured and coupled to the one or more light sources, the inputoptics, the output optics and the detectors in order to measure opticalspectroscopy of the sample. The processor can be coupled to the firstlight source to control the generation of light for TIR measurements.The processor can be coupled to the second light source to control thegeneration of light for the transmission measurements. The processor canbe coupled to the first input optics and first output optics to controlthe input and output optics of the TIR measurements as appropriate, forexample when the input and output optics comprise one or more movable orelectro-optical components such as shutters, gratings, etalons, mirrors,lenses, Bragg cells, prisms or wavelength selective filters, forexample. The processor can be coupled to second input optics and secondoutput optics to control the input and output optics of the bulktransmission measurements as appropriate, for example when the input andoutput optics comprise one or more movable or electro-optical componentssuch as shutters, gratings, etalons, mirrors, lenses, Bragg cells,prisms or wavelength selective filters, for example.

The processor can be coupled to the first detector to measure the lightfrom the TIR measurement and the second detector to measure light fromthe bulk transmission measurement. The detectors of the apparatus 500such as the first detector 545 and second detector 550 may comprise oneor more of many known detectors such as a one or more of photodiode, aphototransistor, a charge coupled device (hereinafter “CCD”) array, orconducting metal oxide semiconductor arrays (hereinafter “CMOS” arrays),for example. The detectors or the processor may comprise analog todigital conversion circuitry to provide a digital measurement signal tothe processor.

The light sources of the apparatus 500 such as the first light source515 and second light source 520 may comprise one or more of many knownlight sources such as lamps, diodes, lasers, laser diodes, tunablelasers, optical parametric oscillators, providing a suitable wavelengthof light, for example in the mid infrared as described herein. In manyembodiments, one or more of the light source or the input optics iscoupled to the processor to vary the wavelength of light, for example.

The apparatus 500 may comprise similar components connected to theprocessor for the second measurement stage. Alternatively, the firststage and the second can be interchangeable such that the firstmeasurement stage can be removed and replaced with the secondmeasurement stage.

The first measurement stage may comprise the prism 110, sample container400 and movable transparent support 350 as described herein. The stagemay comprise a coil 560 embedded in the container to heat the sample 30as described herein, and an actuator 565 coupled to the movabletransparent support to pressurize the sample. A pressure sensor and atemperature sensor can also be provided on the measurement stage tomonitor the pressure and the temperature of the sample. The prism maycomprise a Dove prism having the measurement surface 100 to provide theevanescent wave and bulk transmission measurements as described herein.

The processor comprises a tangible medium to store the instructions,such as one or more of random access memory (hereinafter “RAM”), readonly memory (hereinafter “ROM”), flash memory, gate array logic, a gatearray, or a field programmable gate array, for example. The processormay comprise a processor system comprising a plurality of processor incommunication with each other, for example. In many embodiments theprocessors communicate with each other with one or more knowncommunication methods and apparatus such as wireless communication, ashared bus, a shared drive, serial communication, the Internet, andcombinations thereof, for example.

The changes in one or more components of blood disclosed herein can bemeasured in one or more of many ways. For example, the changes can bedetected using a one or more of many types of chemical analyses, such asspectroscopy and spectrometry, for example. In many embodiments,spectroscopy methods and apparatus are configured for measuring bloodcomponents, such as changes in molecular conformation in blood cellmembranes and blood proteins. Examples of suitable spectroscopy methodsand apparatus suitable for incorporation in accordance with embodimentsdisclosed herein include one or more of vibrational spectroscopy, eithermid-infrared or near-infrared absorption or reflection spectroscopy, orRaman spectroscopy, and combinations thereof. In many embodiments,vibrational spectroscopy methods and apparatus are configured to measurelevels of metabolites and proteins in blood. In many embodiments, massspectrometry methods and apparatus are configured to measure one or morecomponents of blood as described herein. In many embodiments, nuclearmagnetic resonance (hereinafter “NMR”) methods and apparatus can beconfigured to determine the presence of biomarkers of the one or morecomponents of blood as described herein.

The spectroscopy may comprise one or more of molecular spectroscopy(infrared, near-infrared, UV, Raman, Surface enhanced Raman, resonanceRaman, fluorescence, NMR, terahertz, far infrared, circular dichroism).Additional or alternative testing can be used such as a mechanical test(mechanical stiffness), or through a thermal property analysis (thermalgravimetric analysis TGA), for example, or rheology, for example.

FIG. 6 shows a method 600 of measuring biomarkers of blood such as bloodpressure biomarkers, in accordance with embodiments.

At a step 605, a blood sample is provided. The blood sample may comprisea single drop of blood.

At a step 610, the blood is separated into a first component and asecond component.

At a step 615, the sample is placed on the support.

At a step 620 a biomarker of the blood sample is measured.

At a step 625, the first component is measured with one or more of TIRor transmission spectroscopy.

At a step 630, the second component is measured with one or more of TIRor transmission spectroscopy.

At a step 635, the sample is pressurized.

At a step 640, the sample is heated.

At a step 645, the sample profile is measured over time with a pluralityof measurements.

At a step 650, a first light beam is generated with a first lightsource. The first light beam may comprise a TIR light beam as describedherein.

At a step 655, the first light beam is transmitted through first inputoptics.

At a step 660, the sample is coupled with the first light beam.

At a step 665, the first light beam is transmitted through the firstoutput optics.

At a step 670, the sample is measured with the first detector.

At a step 675, a second light beam is generated with a second lightsource. The second light beam may comprise a transmission light beam formeasuring a bulk thickness of the sample as described herein.

At a step 680, the second light beam is transmitted through second inputoptics.

At a step 685, the sample is coupled with the second light beam.

At a step 690, the second light beam is transmitted through the secondoutput optics.

At a step 695, the sample is measured with the second detector.

At a step 700, each of the components of the sample is measured. Forexample each component can be measured with two measurement channels asdescribed herein.

At a step 705, the data are processed.

At a step 710, a blood biomarker such as a blood pressure biomarker isidentified. For example, the presence of the biomarker can be determinedin order to establish the presence or absence of a biomarker.

At a step 715, the subject is treated.

The method 600 discloses a method of measuring blood pressure inaccordance with embodiments. A person of ordinary skill in the art willrecognize many variations and modifications based on the disclosureprovided herein. For example, some steps may be added or removed. Someof the steps may comprise sub-steps, and many of the steps can berepeated.

The processor as described herein can be programmed with one or moreinstructions to perform one or more of the steps of the method 600 ofmeasuring blood pressure of the subject, for example.

Therefore, the above steps are provided as an example of a method ofmeasuring blood pressure of the subject in accordance with embodiments.

In many embodiments, a plurality of biomarkers is measured to identifythe presence of high blood pressure of the subject. For example, a firstbiomarker can be measured and a second biomarker can be measured. Inmany embodiments, an amount of the first biomarker increases in responseto the high blood pressure and an amount of the second biomarkerdecreases in response to the high blood pressure. Alternatively, amountsof both biomarkers can increase, or both amounts can decrease, forexample. In many embodiments, a plurality of three or more biomarkers ismeasured, and an amount of a first at least one biomarker increasesabove a threshold amount to identify the high blood pressure and asecond amount of a second at least one biomarker decreases below athreshold amount to identify the presence of the high blood pressure.

The methods and apparatus as described herein can be combined in one ormore of many ways to measure one or more biomarkers of high bloodpressure, and the embodiments disclosed herein provide examples, and aperson of ordinary skill in the art will recognize many modificationsbased on the disclosure provided herein.

In many embodiments, one or more processors can be configured withmachine learning software in order to correlate changes in the blood asexhibited in changes in the spectral patterns, quantitatively with highblood pressure. This software can use one or more the known tools ofbiostatistics, such as principle components analysis (PCA), principlecomponents regression (PCR), partial least squares regression (PLS),classical least squares (CLS), multivariate curve resolution (MCR),neural networks, et cetera, for example.

In many embodiments, the biomarker for blood pressure comprises apositive marker for blood pressure such that the presence of thebiomarker above a threshold amount indicates that the subject has highblood pressure. Alternatively, the biomarker for blood pressurecomprises a negative biomarker for blood pressure such the presence ofthe negative biomarker above a threshold amount indicates that thesubject does not have high blood pressure. In many embodiments, aplurality of biomarkers are measured in order to identify the presence(or absence) of high blood pressure.

The positive or negative biomarkers, and combinations thereof, can beidentified in one or more of many ways as described herein, such as withPCA, PCR, MCR, CLS, PLS or neural networks, for example.

In many embodiments, the recent central aortic pressure encompasses atleast about one day of blood pressure, such that the measure comprisesan integral of subject blood pressure over at least about one day basedon a single blood draw. In many embodiments, the recent central aorticpressure may comprise an integral of blood pressure over a period oftime of about 3 to 4 months. The recent blood pressure may comprise oneor more of a daily value or a 3-4 month period to determine long-termhealth and wellness and property therapeutic value of druginterventions, and durations in between for example. In manyembodiments, the recent blood pressure comprises at least about a 24hour duration in order to average out diurnal variations.

In many embodiments, the biomarker comprises one or more of thefollowing:

Adenosine diphosphate, one or more transmembrane proteins (such as Band3, Aquaporin 1, Glut1, ICAM-4, BCAM, Ankyrin, Band 4.1, Tropomyosin,Actin, or glycophorin), one or more proteins of the membrane skeleton(such as spectrin), one or more lipids of the red blood cell membrane, arelative ratio of the one or more lipids of the red blood cell membrane,or biomaterial deposited on the surface of the red blood cell membrane.Lipids in the RBC membrane include Phosphatidylcholine (PC);Sphingomyelin (SM) in the outer monolayer, and Phosphatidylethanolamine(PE), Phosphoinositol (PI) (small amounts) and Phosphatidylserine (PS)in the inner membrane. Approximately half the mass of the RBC membraneis proteins and half is phopholipids. The ratio of protein to lipid maychange with high blood pressure, or the relative ratio of various lipidsmay vary. For example the ratio of Phosphatidylcholine to Sphingomyelinmight be 60:40 in a healthy individual, but may change to 50:50 in highblood pressure. Or the ratio of total lipid to total protein may changefrom 50:50 in a healthy individual to 60:40 in high blood pressure.

The biomarker may comprise one or more of specific changes to thesecondary structure of the transmembrane proteins, the proteins of themembrane skeleton of the red blood cell, or changes to the compositionand relative ratios of membrane lipids of the red blood cell membrane,and combinations thereof, for example. Alternatively or in combination,the biomarker may comprise biomaterial coated on the surface of the redblood cells that has been deposited by contact with biomaterials insidethe vasculature, for example deposited in response to abrasive contact.In many embodiments, the biomarker comprises one or more of a change tothe protein composition of the red blood cell membrane, a change to thestructure of the red blood cell membrane, a change to the structure orcomposition of the lipids of the red blood cell membrane, an endogenousbiomaterial deposited onto the outside of the red blood cell throughcontact during flow of the cells through the vessels, or a foreignbiomaterial deposited onto the outside of the red blood cell throughcontact during flow of the cells through the vessels, for example.

FIG. 7 shows a cross section of a red blood cell 40 in accordance withembodiments. The circular cross section shows structures of the redblood cell membrane 46, membrane proteins 50, and structural proteins 54within the red blood cell. The circular cross sectional view shows thelipid bi-layer 48 of the red blood cell membrane, which may comprise aphospholipid bi-layer for example, cholesterol, and phosphatidylcholine, for example. The ratio of components of the lipid bi-layer canbe measured in accordance with embodiments. The membrane protein 50 maycomprise one or more of many known membrane proteins, such astrans-membrane proteins 52, for example. The membrane protein maycomprise one or more of Band 3, Ankyrin, CD47, Rh, or Glycophorin, forexample. For example, the red blood cell membrane may comprisetrans-membrane protein such as Ankyrin extending through the membrane inorder to transmit ions for example. The red blood cell membrane maycomprise interior protein such as spectrin protein, for example aspectrin network 58 extending substantially along an interior of thecell membrane and interior to the cell wall.

In many embodiments, the red blood cell membrane corresponds to a fluidmosaic model of biological membranes, and membranes in addition oralternative to the red blood cell membrane can be measured. The membranemay comprise membrane proteins which are mobile within the phospholipidand cholesterol layer. The spectrin network of the membrane skeleton 56provides strength to the red blood cell membrane by interacting with theother proteins of the membrane as described herein.

In accordance with embodiments, changes in the red blood cell membraneand structures associated with the red blood cell membrane can bemeasured. For example, lipids can be measured and changes in lipids,lipid ratios and changes in lipid ratios, proteins can be measured,protein ratios can be measured and protein to lipid ratios can bemeasured.

The measurement in the analysis of the red blood cell membrane can beperformed in one or more many ways, for example, with principalcomponent analysis (PCA).

FIG. 8 shows an enlarged view of the red blood cell membrane 46 placedon a support structure 105 for measurement in accordance withembodiments. The support comprises an optically transmissive material asdisclosed herein and the evanescent field 125, an evanescent vectorextending at least partially beyond an upper or measurement surface 100of the support on which the red blood cell membrane reside. A light waveis infinite on the upper surface of the support at an incidence angle120 of theta. The measurement light 115 comprises a wavelength lambda.The depth 135 of the evanescent field comprises a zone of sensitivity130. The zone of sensitivity can be adjusted based on combinations ofone or more of the incidence angle Θ (theta) and the wavelength of lightλ (lambda), in order to limit the depth of the zone of sensitivity ofthe measurement. The limitation of the measurement depth providesmeasurement of the cell membrane on the surface, such as the red bloodcell membrane and corresponding structures such as the trans-membraneproteins 52 and the structural proteins 54, and inhibits measurement ofdeeper structures such as hemoglobin 60, for example. The measuredstructures of the membrane can be structures of the intact cell, and maycomprise one or more of the trans-membrane protein Ankyrin and thestructural protein Spectrin, for example.

The red blood cell may comprise an intact red blood cell as describedherein. The zone of sensitivity can inhibit measurement of hemoglobinwith a zone of sensitivity corresponding substantially to the red bloodcell membrane, the lipid bi-layer of the red blood cell membrane,trans-membrane proteins of the red blood cell membrane, and structuralsupport proteins of the red blood cell membranes, such as, spectrin forexample. In many embodiments hemoglobin is positioned within the intactred blood cell at locations away from the red blood cell membrane suchthat the zone of sensitivity does not extend substantially into ahemoglobin molecule and, for example, does not extend across ahemoglobin molecule within the red blood cell membrane. Theseembodiments can provide specificity to the measurement and localizationto the red blood cell membrane.

In accordance with embodiments described herein, ratios of components ofthe red blood cell or other membranes of another cell can be measured.For example, the ratio of phosphatidyl choline to cholesterol can bemeasured. The ratios of phospholipids to other components can bemeasured such as the ratio of one or more lipid components to a ratio ofone or more protein components.

The components of the red blood cell membrane can be measured in one ormore of many ways, and reference is made to spectroscopy merely by wayof example in accordance with embodiments.

Alternatively or in combination, rheology can be used to measure thecomponents of the red blood cell membrane. The rheology measurementapparatus may comprise one or more capillary tubes having a diametersize to inhibit flow and limit flow and provide at least some resistanceto blood flow, for example. The rheology of the plurality of red bloodcells measured may correspond to structural aspects of the surfaceexterior, which can be affected by one or more substances on the surfaceof the red blood cells, for example.

The rheology components can be measured with a transform function andtransfer function. For example, the flow characteristics of the redblood cells of the blood sample through capillary tubes can be measuredand the impedance profiles determined for plurality of frequencies inorder to determine a transform function spectra. The impedance of theblood flow through the one or more capillary tubes is measured at aplurality of frequencies in order to provide a spectrum. The mechanicalspectral data can be combined with optical spectral data as describedherein. Alternatively, the mechanical spectral data can be used todetermine the presence of one or more biomarkers.

The rheology embodiments are well suited for combination with theoptical embodiments. For example, the aggregation of red blood cells canaffect the measured flow parameters of the blood, and the aggregation ofthe red blood cells can also be related to one or more surfacecomponents of the red blood cell membrane as described herein, forexample.

In many embodiments the analysis comprises a principal componentanalysis (PCA), comprising the plurality of dimensions and thedimensions may comprise orthogonal eigenvectors for example. A person ofordinary skill in the art will have at least some familiarity with PCA,and can determine the presence or absence of biomarkers from a bloodsample with PCA, for example.

FIG. 9 shows an apparatus 900 comprising a database 905 and a userinterface 910 to determine identify markers of red blood cells relatedto health in accordance with embodiments. The apparatus 900 mayoptionally comprise one or more components of the measurement apparatus970 as disclosed herein, such apparatus 500, for example. The userinterface comprises a display 915 connected to a processor 930 such thatthe user can view the biomarker data 920 on the display. The userinterface also comprises one or more user input fields 925. Theprocessor may comprise a processor system 935 and can store data of thedatabase for the user to see information of the database on the display.The processor comprises a tangible medium 940 storing instructions ofthe database, such that the user can see the information on the display.The tangible medium may comprise a computer readable medium having oneor more of many known forms such as random access memory (RAM), readonly memory (ROM), compact disc CD-ROM, flash RAM. The processor maycomprise one or more of a plurality of Internet based cloud servers 945,a remote back end server 950, or a local server 955, or a localprocessor 960 for example. The display may comprise a display of a handheld processor such as a smart phone in communication with a server, forexample. Each of the components of the apparatus 900 can be connected inone or more of many ways as will be apparent to a person of ordinaryskill in the art, and each of the components as shown can be connectedto another component, either directly or indirectly through othercomponents and communication pathways as disclosed herein.

The measurement apparatus as described herein can be combined with thedatabase and user interface in many ways. In many embodiments, data fromthe measurement apparatus is shown on the display. The data shown on thedisplay may comprise data of the amplified red blood cell measurementsignal as described herein. In many embodiments, output of the processorsystem, can be shown on the display, in accordance with steps of one ormore methods as described herein, and the one or more processors maycomprise instructions to perform the one or more method steps and outputthe data on the display. In many embodiments, the data output to theuser interface comprises cell membrane amplification data as describedherein, such as data of a plurality of cell membranes shown on thedisplay. The data of the plurality of cell membranes may compriseevanescent wave data of a plurality of intact red blood cell membranes,for example. In many embodiments, amplified data comprises amplifiedcell membrane data of a plurality of washed cells, such asgravimetrically separated washed red blood cells as described herein.The data shown on the display to the user may comprise one or morebiomarkers of health from the gravimetrically separated and washedmembranes of intact red blood cells, for example. The one or moreprocessors as described herein can be configured to with instructionsstored on a tangible medium such as a computer readable medium toprovide the data on the display.

FIG. 10 shows light 115 entering germanium optical structure 110 (indexof refraction n=4) at an incident angle 145 of 80 degrees. This incidentangle results in total internal reflection and a very shallow 1/epenetration depth 135 of the resulting evanescent wave 140 into thesample. The sample can comprise red blood cells 40, as shown. The endsof the germanium can be anti-reflection (AR) coated. The germaniumoptical structure may comprise one or more inclined prism surfaces asdescribed herein, and may comprise waveguide as described herein, forexample.

Table 1 shows penetration depths for various angles of incidence andwavelengths in different sampler surfaces (diamond, silicon, andgermanium), in accordance with embodiments.

TABLE 1 Penetration Depths. angle of depth of sampler incidencepenetration sample window wavelength surface (degrees) (microns) indexn2 index n1 (microns) diamond 35 0.958 1.33 2.39 2 diamond 45 0.305 1.332.39 2 diamond 75 0.169 1.33 2.39 2 diamond 35 3.354 1.33 2.39 7 diamond45 1.068 1.33 2.39 7 diamond 75 0.590 1.33 2.39 7 diamond 35 4.792 1.332.39 10 diamond 45 1.526 1.33 2.39 10 diamond 75 0.843 1.33 2.39 10silicon 35 0.221 1.33 3.42 2 silicon 45 0.158 1.33 3.42 2 silicon 750.105 1.33 3.42 2 silicon 35 0.773 1.33 3.42 7 silicon 45 0.552 1.333.42 7 silicon 75 0.368 1.33 3.42 7 germanium 35 0.169 1.33 4.02 2germanium 45 0.127 1.33 4.02 2 germanium 75 0.087 1.33 4.02 2 germanium35 0.591 1.33 4.02 7 germanium 45 0.443 1.33 4.02 7 germanium 75 0.3051.33 4.02 7 germanium 35 0.845 1.33 4.02 10 germanium 45 0.634 1.33 4.0210 germanium 75 0.436 1.33 4.02 10

FIG. 11A shows a sample gravimetric washing container or holder 400 andspectrometer 200 to measure a blood sample 30. In many embodiments, thecontainer is coupled to the spectroscopic measurement apparatus asdisclosed herein. The internally reflective structure may comprise awaveguide 250 optically coupled to the cells such as red blood cells 40placed in the container. The container comprises a vertically extendinglength 405 to provide gravimetric separation. A cover or lid 410 extendsover an upper portion of the container. The cover comprises an opening415 formed in the cover. A capillary tube may extend to the opening inthe cover.

In many embodiments the measurement apparatus comprises a support fixedin relation to the spectrometer optics such that the container can beremoved. The support may comprise a lower support 425 fixed in relationto the optics of the spectrometer such that the container can be placedon the lower support. The container may comprise an upper support 420affixed to the container such that the container can be removed. Thefixed lower support can be sized to receive a portion of the containerin order to engage the upper support. The measurement apparatuscomprises input coupling optics 230 such as a lens to couple the lightsource 210 of the spectrometer to the waveguide structure of thecontainer, and output coupling optics 240 such as lens to couple theoutput of the waveguide structure to photodetectors 220.

In many embodiments, the upper support, the lower support and thecoupling optics are arranged to couple the waveguide to the couplingoptics when the upper support rests on the lower support. In manyembodiments, the upper support comprises a lower flange or rim of thecontainer sized and shaped to be received with the lower support andalign the waveguide structure with the coupling optics when received inthe lower support.

Gravimetric separation can be performed in a solution 430. The solutioncan be isotonic compared to blood, or can be hypertonic or hypotoniccompared to blood, and combinations thereof. Hypertonic or hypotonicsolution can result in conformational changes in red blood cells whichmay be useful for subsequent analysis. The solution can comprise saline.The solution can comprise components with known spectral bands forspectroscopic calibration, such as for example ethanol or methanol, andeach spectra can be determined in response to the known spectral bands,for example. A container, of solution can be positioned on top of aprism or other spectrometer sampling element, for example as shown inFIG. 11A. The container can be shaped in one or more of many ways andmay comprise a cylindrical column, for example. The container comprisesa vertically extending length sufficient to allow gravimetric separationof the red blood cells from other components of the red blood cellsample such as the serum.

In many embodiments, the container column is placed on top of awaveguide structure such as prism, for example. The container maycomprise a lower membrane having a thickness less than the 1/e depth ofthe evanescent wave in order to measure the blood sample through themembrane A thin optically transmissive material can be located on theupper surface of the waveguide, in which the thin material comprises athickness less than the 1/e penetration depth of the evanescent wave,for example.

The waveguide structure can be dimensioned in one or more of many waysas disclosed herein. In many embodiments the waveguide comprises a firstend 252 to receive light energy and a second end 254 to transmit lightenergy. The wave guide may comprise an upper surface 256 on an upperside oriented toward the sample and a lower surface 258 on a lower sideoriented away from the sample. The waveguide may comprise a thicknessextending between the upper surface and the lower surface. In manyembodiments the waveguide comprises a length extending in a direction ofpropagation from the first end to the second end. The waveguide maycomprise a width transverse to the length. In many embodiments, thewaveguide comprises a width greater than the thickness and a lengthgreater than the width in order to provide a plurality of internalreflections of the measurement light energy from the upper surface ofthe waveguide in order to amplify the optical signal transmitted fromthe second end of the waveguide.

The ends of the waveguide can be configured in one or more of many waysand may comprise surfaces extending perpendicular to a long dimension ofthe waveguide, or inclined at an angle so as to comprise prismaticsurfaces. In many embodiments, the waveguide comprises a prism, forexample a dove prism as described herein.

Alternatively or in combination, the removable container 400 maycomprise the waveguide structure 250. The waveguide structure can beremovable with the container and located on the lower end of thecontainer. The container can be removed or placed with the upper lidwith comprising an upper hole or capillary for introducing sample intothe container. A sample comprising red blood cells can be introduced tothe container, and the relatively heavier red blood cells can beseparated gravimetrically and settle onto the sampling surface eitherbefore or after the container has been placed on the support.

In many embodiments, the red blood cells can be washed by the solutionduring the gravimetric separation, such that potential contaminants canbe removed from the measurement.

FIG. 11B shows a container 400 as in FIG. 11A removed from thespectrometer. In many embodiments, the container comprises a removablecontainer, such that the container comprises a single use consumableitem and the spectrometer components can be reused. In many embodiments,the apparatus comprises a fixed support structure that engages aremovable support 420 affixed to the container. The container can beaccurately coupled to the spectrometer with a support structure such asa flange, collar, or other support on the container itself. Thespectrometer and associated light source and detector can be used totake measurements with the waveguide 250 on the lower end of thecontainer.

In many embodiments the lower support is fixed in relation to the opticsof the spectrometer, such that placement of the container comprising thewaveguide can be aligned with the measurement optics when placed inorder to provide accurate spectroscopic measurements. One or more of theupper support or the lower support can be sized and shaped in order toposition the waveguide with a position and orientation for measurementof the cells on the lower surface of the container, for example.

Additional components can also be added to the container to alter thesample if helpful. For example, gluteraldehyde can be added to thecolumn to alter red blood cell membrane structure.

In many embodiments, a plurality of gravimetric separation containers isprovided, in which each container of the plurality comprises a removablesingle use consumable container.

In many embodiments, spectra can be measured from the sample andstatistical analysis methods can be used to generate a plurality offactors. The plurality of factors may comprise a plurality of functionsupon which the data can be projected in order to determine the amount,or concentration, of each function in the sample. The factors can beorthogonal or non-orthogonal, for example. The analysis can comprise oneor more of principle components analysis (PCA), principle componentsregression (PCR), classical least squares (CLS), multivariate curveresolution (MCR), partial least squares regression (PLS), neuralnetworks, or other biostatistical or chemometric approaches, forexample. In many embodiments, the factors are orthogonal to each other.Alternatively, at least some of the factors may comprise non-orthogonalfactors. One or more relevant factors can be identified, and the redblood cell status or history can be determined in response to the one ormore relevant factors. In many embodiments, the history of the red bloodcells comprises a control of the red blood cells of the subject, forexample a control of a condition such as high blood pressure of thesubject. The one or more relevant factors may comprise one or morestatistically relevant factors, for example.

In many embodiments, a plurality of spectral bands comprise peaksrelated to structure of the cell such as protein structure of the redblood cell. The Amide I band of frequencies comprising the Amide I peakmay correspond to alpha helix protein structures of the proteins of thered blood cell membrane. The Amide II band of frequencies comprising theAmide II peak may correspond to beta-sheet protein structures of thecell membrane. The band of frequencies comprising the Amide III band maycorrespond to disordered protein structures of the cell membrane. Thedetermination of factors corresponding to these spectral bands and theshifts of peaks and intensities of these spectral bands in response tothe measure spectra can be used to determine the one or more biomarkersof the cellular membrane such as the red blood cell membrane.

In many embodiments, deformation of the red blood cell membrane resultsin measurable spectroscopic changes to the red blood cell membrane thatcan be measured as described herein. The measurable changes may compriseshifts in the spectral peaks as disclosed herein. The spectroscopicchanges to the red blood cell membrane can be substantiallyinstantaneous, for example upon deformation of the red blood cellmembrane. Alternatively, the spectroscopic changes to the red blood cellmembrane may comprise changes occurring over the history of the redblood cell, for example over a long term three month historycorresponding to the 90 to 120 day functional lifetime of the red bloodcell.

In many embodiments the factors can be used to determine the history ofthe red blood cell, and can be used to determine the long term controlof a condition such as hypertension, for example. The long term controlmay comprise a conformational change to the red blood cell membrane thatcan be determined with at least one factor as disclosed herein, forexample with a relationship among factors as disclosed herein.

In many embodiments, the biomarker amplifies an optical spectral signal.The biomarker may comprise a change to cell membrane, such as aconformational change to a protein of a red blood cell membrane or aratio of components of the red blood cell membrane as disclosed herein,for example. As the red blood cells comprise a long dimension that canextend along the measurement surface and optically couple the red bloodcell membrane to the evanescent wave measurement surface, the measuredsignal can be amplified substantially. In many embodiments, a substancerelated to the health status of the subject may not itself be detectablewith the spectral measurements. The measurement of the red blood cellmembrane can provide, however, an optical spectral signal to determinethe presence of the substance. For example, spectral changes of the redblood cell membrane provided with aspirin as disclosed herein can beused to identify a response of the red blood cell membrane to aspirin,even though the presence of aspirin itself may not be detectablespectroscopically in some embodiments. The optical waveguide can beconfigured to provide a plurality of reflections from the evanescentwave measurement surface in order to provide an increased amplificationof the measured evanescent wave signal.

FIG. 11C shows a tube 440 to draw a sample. The draw tube can be used todraw a blood sample 30, such as a sample from a pool of blood on anexternal surface such as an external surface of a finger 20. In manyembodiments, the draw tube comprises a permeable membrane having poressized to wash the sample. Alternatively, the draw tube may comprise animpermeable membrane for placement of the sample in a container asdescribed herein.

FIG. 11D shows sample delivery and cell washing with a removable sampleholder 400 as described herein. The sample holder 400 may comprise acontainer 450 coupled to an inlet tube 470 and an outlet tube 475. Theinlet tube can provide a rinse solution 480 and the outlet tube can passrinsate 485 from the sample container. The sample container may comprisean inner portion 455 and an outer portion 460 with the permeablemembrane 465 extending therebetween, in order to provide cross-flowfiltration, for example. The inlet tube can be connected to the innerportion of the sample container and the outlet tube can be connected tothe outer portion of the sample container. An attenuated totalreflection (ATR) waveguide crystal 250 can be located on a lower end ofthe sample container. The cells of the sample 30 can be retained in thedraw tube and deposited onto the ATR crystal for measurement asdescribed herein. The rinsate column has the advantage of removingnon-cellular material from the measured sample, such as serum andpotential lysate.

The sample draw tube 440 as in FIG. 11C comprising the semipermeablemembrane 465 can be used to collect a blood sample 30, and the draw tubecomprising the permeable membrane can be placed in an annular container450 comprising a column of fluid. Alternatively, a drop of blood can beplaced on an upper end of the draw tube in order to receive the bloodsample with the tube. The permeable membrane may comprise an approximatepore size of about 5 um in order to inhibit passage of cells through thepores and to allow passage of water and molecules, for example, in orderto wash the sample.

A cover 490 can be placed over the annular container in order to washthe sample. The cover may comprise an inlet tube extending from thecover. The cover may comprise an opening formed therein coupled to alumen 445 of the tube 440 placed into the container 450, to pass fluidfrom the tube through the cover and into the draw tube. An outlet can becoupled to an outer annular portion of the annular container defined bythe draw tube. The draw tube can be placed within the annular containersuch that the lumen of the draw tube defines a first inner portion ofthe annular container within the draw tube and a second outer annularportion of the annular container outside the draw tube.

The outlet tube can be connected to a lower portion of the outer portionof the container as shown. Alternatively, the outlet tube can be coupledto an upper portion of the sample container, and may be integrated withthe cover, for example, such that both the inlet tube and the outlettube extend from the cover.

The ATR waveguide crystal as described herein can be located on a lowerend of the annular container, and coupled to spectrometer optics, suchthat the sample container comprises a removable sample container among aplurality of sample containers as described herein. The waveguide can belocated on a lower end of the draw tube, for example.

The sample holder 400 comprising the container has the followingadvantages:

Washes the serum and potential lysate from the cell membranes

Packs cells onto ATR crystal

Disposable

The sample container can be used with one or more of the followingsteps:

Wash Cycle

-   -   Washes serum and potential lysed material into rinsate column;

Drain Cycle

-   -   Drains a the rinsate column and in addition drains a majority of        the membrane straw leaving a layer of cells on ATR crystal; and

Measure Cycle.

-   -   Begin spectroscopic measurement when sufficient cell membrane        signal exists

FIG. 12 shows a method 1200 of analyzing a sample. At a step 1210, thesample is acquired as described herein. At a step 1220, the acquiredsample is separated as described herein, for example with gravimetricseparation and washing. At a step 1230, spectra are measured from thesample and statistical analysis methods can be used to determine thehistory of the cell such as the red blood cell. The analysis methods maycomprise one or more of principle components analysis (PCA), principlecomponents regression (PCR), multivariate curve resolution (MCR),classical least squares (CLS), partial least squares regression (PLS),neural networks, or other biostatistical or chemometric approaches, forexample. At a step 1240, a plurality of factors is generated. Thefactors can be orthogonal to each other, for example. At a step 1250,one or more relevant factors is identified. At a step 1260 the red bloodcell history is determined in response to the one or more relevantfactors. At a step 1270, the above steps are repeated.

FIG. 12 shows a method of analyzing a sample in accordance withembodiments. A person of ordinary skill in the art will recognize manyadaptations and variations in accordance with the embodiments disclosedherein. For example one or more steps can be deleted. Steps can beadded, and some steps can be repeated. At least some of the steps maycomprise sub-steps.

The method 1200 can be embodied with instructions of a processor on atangible medium. The processor may comprise one or more a computer, acloud computer, a computer network, a digital processor, a digitalsignal processor, gate array logic, field programmable gate array,programmable array logic. The tangible medium comprises may comprise astorage structure to store instructions of the processor, for example acomputer readable memory such as flash memory, random access memory or ahard disk drive.

The methods and apparatus disclosed herein can be configured in one ormore of many ways to measure vibrational spectroscopy of the sample,such as infrared (IR) spectroscopy, near infrared spectroscopy, visiblespectroscopy, Raman spectroscopy, nuclear magnetic resonance (NMR)spectroscopy, total internal reflection (TIR) spectroscopy, TIR-IRspectroscopy, transmission spectroscopy, transmission IR spectroscopy,or transmission near-IR spectroscopy.

The methods and apparatus disclosed herein can be configured todetermine spectral changes in a blood sample in response to one or moreof drying of the blood sample, washing of the blood sample,hyper-molality of the blood sample, hypo-molality of the blood sample,temperature of the blood sample, heating of the blood sample, cooling ofthe blood sample, pressure of the blood sample, pressurization of theblood sample, and depressurization of the blood sample.

For example, the methods and apparatus described herein can beconfigured to measure spectroscopic data of red blood cells over a timeperiod of a drying process. The red blood cells may be purified andwashed, e.g., resuspended to 20% hematocrit in phosphate bufferedsaline, then subjected to a gradual drying process, and the sample maybe measured spectroscopically as described herein at regular timeintervals. Such a measurement can provide a study of how the chemicalcomposition, protein structure and/or conformation of the red blood cellmembrane changes over a drying process. Work in relation embodimentssuggests that the methods and apparatus as described herein may bewell-suited for the measurement of dried blood samples. Without beingbound by any particular theory, the drying of red blood cells canprovide some enhancement in spectroscopic measurements. For example,since water is known to interfere with infrared measurements, theremoval of water from the sample may improve the spectral signal of thesample of interest. Alternatively or in combination, the removal ofwater from the sample may cause the sample region of interest, e.g., redblood cell membranes, to adsorb on the measurement surface, resulting inan improvement of the spectral signal of interest. Removal of at leastsome water from the blood samples may further inhibit lysing of the redblood cells, such that the red blood cell membranes remain substantiallyintact during measurement. Accordingly, the methods and apparatusdisclosed herein may be configured to identify blood pressure of bloodsamples with at least some water removed from the blood sample, in orderto improve the spectral signal of the red blood cell membranes. Forexample, the blood samples with about 50% of the water of the bloodsample removed may be measured.

The methods and apparatus as described herein may also be configured tomeasure spectroscopic data of blood samples at different osmolalities,which may cause red blood cells to shrivel, expand, lyse, or otherwiseundergo conformational changes. A plurality of spectra may be obtainedfrom the blood sample, each spectra corresponding to a differentosmolality.

The processor as described herein can be configured to identify acondition of the patient, such as one or more of high blood pressure ormalaria, for example. The processor system can be configured to analyzethe sample as described herein, for example with one or more of a leastsquares fit or a classic least squares fit, for example. Spectral shapescan be associated with blood pressure, such as mean arterial bloodpressure, systolic blood pressure, diastolic blood pressure, or pulsepressure, for example. The processor may comprise instructions toidentify high blood pressure of the patient in response to one or morespectral signatures as described herein, for example by determining aplurality of spectral factors as described herein

The methods and apparatus disclosed herein can be used to identify acondition of a patient in response to spectra of a blood sample of thepatient. The thus-identified condition may be used to determine anappropriate course of treatment for the patient, such as to identify adrug to administer to the patient or to determine the amount of saiddrug to administer to the patient. For example, the processor of theapparatus may comprise instructions to determine an amount of drug toprovide to the patient in response to spectral data of the patient'sblood sample. One or more clinical trials may be conducted to validatethe identification of the course of treatment using spectralmeasurements of a patient's blood sample. For example, the amount ofdrug for administration to the patient, determined using the measurementof blood spectral data, may be validated with one or more clinicaltrials.

The methods and apparatus disclosed herein may be suitable forincorporation with clinical trials. For example, a method of performinga clinical trial to evaluate a safety and/or efficacy of a treatmentwith a device and/or drug may comprise using the measurement apparatusas described herein to measure blood samples of patients.

The methods and apparatus can be configured to provide a differentialmeasurement of the sample, with first spectra measured without thesample to calibrate the instrument and second spectra measured with thesample. The calibration measurements can be obtained with the sampleholder placed in the spectrometer and without the sample.

The sample can be measured without over fitting the data, for example.

While many computation methods can be used as described herein,classical least squares can be used to fit bands and functional groupsand provide functional group analysis, for example. Alternatively or incombination, partial least squares fitting can be used. Known factorssuch as one or more of water or water vapor can be added to the sample apriori, for example. Augmented classical least squares can be used toanalyze the spectral data.

The methods and apparatus as described herein can be configured withinstructions to provide augmentation of the calibration space. While thecalibration space augmentation can be performed in one or more of manyways with the factors and functions methods as described herein, thecalibration space augmentation may comprise one or more of an augmentedclassical least squares of the calibration space data, an augmentedpartial least square of the calibration space data, or an multivariatecurve resolution of the calibration space data. An iterative fit can beperformed to linearly independent spectral data sets, for example. Aspectral signature can be developed for one or more of the calibrationspace data or the blood sample data, for example. The spectral signatureof the calibration space data can be used for later analysis of theblood sample as described herein, for example with one or more ofpartial least squares, augmented classical least squares, multivariatecurve resolution, or other chemometric approach as described herein, forexample.

FIG. 22 shows a method 2200 of spectral data analysis suitable forincorporation with embodiments. A variant of Classical Least Squares(CLS) may be used to build calibration models and predict blood pressurevalues based on red blood cell spectra. This CLS variant has beenreferred to as Augmented CLS and can often be performed during theprediction process. CLS assumes Beer's law behavior (A=CK+E_(A)), whereA is the absorbance spectra, C is a matrix of concentrations, K is thepure component spectra and E_(A) are the spectral residuals (anythingunmodelled by linear combination of C and K). Red blood cell spectraobtained using a measurement apparatus as described herein can beconverted to absorbance by taking the minus Log 10 of the ratio of thered blood cell spectra to a close-in-time instrumental backgroundspectrum. Since CLS tries to minimize E_(A), all sources of spectralvariation need to be modelled through the concentrations (C) and thepure component spectra (K) in order to produce accurate resultantestimates. The pure component spectrum (K) of an analyte of interest isusually already known; therefore augmentation usually occurs in theprediction process (solving for C). To prevent aberrant spectralvariation (spectral variation not associated with the analyte ofinterest) from affecting the CLS model, the model may be proactivelyaugmented with spectral component(s) associated with these aberrations,so that better concentration estimates of the analyte of interest can beobtained. The augmentation process may be applied during the calibrationprocess, in order to get an accurate estimate of the spectral purecomponent associated with blood pressure.

At step 2202, a concentration matrix C is created to obtain the purespectral component of blood pressure. This concentration matrix can becomposed of blood pressure reference measurements (C_(BP)),concentrations associated spectral variance during the measurement ofthe red blood cell samples but not associated with the red blood cells(C_(S)), and concentrations associated with spectral variance of theinstrument (C_(I)). Concentrations C_(BP), C_(S), and C₁ can be combinedinto one concentration matrix C, and used to estimate the pure spectralcomponents that can be used for later predictions.

At step 2204, the blood pressure reference values (C_(BP)) are obtained.The blood pressure reference values C_(BP) may comprise the mean of theblood pressures acquired over a period of time from a subject, to ensurethe best estimate of the actual sustained blood pressures from thesubject.

At step 2206, the concentrations associated with spectral varianceduring the measurement of the red blood cell samples (C_(S)) areobtained.

At step 2208, previously obtained pure spectral components (K_(S)) areapplied. Spectral components K_(S) may comprise spectral components ofwater, red blood cells, and spectral variation associated with a processapplied to the red blood cells, such as drying.

At step 2210, the concentrations C_(S) are estimated using CLS, from thepseudo inverse of the previously obtained pure spectral components K_(S)and the absorbance spectra A. The pseudo inverse K⁺ of the spectralcomponents K_(S) can be obtained using the equation K⁺={circumflex over(K)}_(s) ^(T)({circumflex over (K)}_(s){circumflex over (K)}_(s)^(T))⁻¹, where {circumflex over (K)}_(s) ^(T) is the transpose of thematrix {circumflex over (K)}_(s).

At step 2212, the concentrations associated with the instrumentvariation (C₁) are obtained.

At step 2214, instrumental background spectra (Bkg) are applied.Background spectra Bkg may be taken during the entire period ofabsorbance spectra (A) data collection. These background spectra cancomprise measurements of air (no sample in sample compartment ofinstrument), or measurements of a sample that most spectrally resemblesthe sample of interest, but is not the actual sample of interest (e.g.,water or saline). These background spectra can be decomposed intospectral factors or components (K_(I)) by using Principal ComponentAnalysis (PCA). The number of these spectral components (K_(I)) can bevaried, such that only the largest sources of spectral variance areexplained by these spectral components (K_(I)).

At step 2216, the concentrations associated with the instrumentvariation C_(I) are estimated using CLS, from the pseudo inverse of theinstrument variation spectral components K_(I) and the absorbancespectra A. The pseudo inverse K⁺ of the spectral components K_(I) can beobtained using the equation K⁺={circumflex over (K)}_(I)^(T)({circumflex over (K)}_(I){circumflex over (K)}_(I) ^(T))⁻¹, where{circumflex over (K)}_(I) ^(T) is the transpose the matrix {circumflexover (K)}_(I).

At step 2218, the calibration model is built by using a CLS calculationto obtain the pure component spectra K of which the component ofinterest resides, from the pseudo inverse of the concentration matrix Cand absorbance spectra. The pseudo inverse C⁺ of the concentrations Ccan be obtained using the equation C⁺=(C^(T)C)⁻¹C^(T), where C^(T) isthe transpose the matrix C. The spectral component of interest can be,for example, the component associated with blood pressure.

At step 2220, the concentration C of the component of interest ispredicted using traditional CLS, from the pseudo inverse of the purecomponent spectra K and the absorbance spectra A. The pseudo inverse K⁺of the spectral components K can be obtained using the equationK⁺={circumflex over (K)}^(T)({circumflex over (K)}{circumflex over(K)}^(T))⁻¹, where {circumflex over (K)}^(T) is the transpose of thematrix {circumflex over (K)}. The concentration C can be, for example,the blood pressure level. Using this prediction model, blood pressuremay be predicted using spectral data of blood samples acquired in thefuture by using traditional or augmented CLS methods.

The method 2200 discloses a method of predicting blood pressure fromspectroscopic data from blood samples, in accordance with embodiments. Aperson of ordinary skill in the art will recognize many variations andmodifications based on the disclosure provided herein. For example, somesteps may be modified, some steps may be added or removed, some of thesteps may comprise sub-steps, and many of the steps can be repeated.

The processor as described herein can be programmed with one or moreinstructions to perform one or more of the steps of the method 2200 ofpredicting blood pressure using blood spectroscopic measurements.Therefore, the above steps are provided as an example of a method ofmeasuring blood pressure of the subject in accordance with embodiments.

Work in relation to embodiments suggests that the methods and apparatusdisclosed herein are well suited to determine early stages of malaria,for example before a ring structure becomes visible under a microscopicview of a blood sample. As malaria can induce changes to the red bloodcell membrane, the spectroscopic analysis of the red blood cell membraneas described herein can be used to identify malaria.

The spectrometer as described herein may comprise a hand held portablespectrometer for example. The spectrometer may comprise an opticalwindow that can be wiped off subsequent to measurement of the bloodsample, and used repeatedly with cleaning, for example. Alternatively,the spectrometer may comprise a consumable single use window componentas described herein, for example.

The methods of sample measurement and analysis as described herein maybe optimized using computational algorithms. For example, one or moresteps of the methods described herein involving the selection of aparameter may be optimized using a genetic algorithm. A geneticalgorithm generally comprises a family of evolutionary search proceduresthat are based upon mechanisms of natural selection and genetics. Agenetic algorithm may apply principles of survival of the fittest tosolve general optimization problems.

A genetic algorithm may be used to optimize one or more steps ofspectral data analysis as described herein. For example, a geneticalgorithm may be applied to select a subset of wavelengths orfrequencies of sample spectra to use in generating a calibration modelto predict blood pressure from red blood cell spectra. A sample spectrumusually comprises a plurality of measurements at a plurality offrequencies, wherein the plurality may comprise hundreds or thousands ofdata points. Therefore, selecting a subset of frequencies that are mostrelevant for predicting blood pressure in building the calibration modelcan enhance the accuracy or predictiveness of the generated calibrationmodel, as well as reduce the computational burden in building thecalibration model and generating predictions.

FIG. 27 illustrates a method 2700 of applying a genetic algorithm toselect a subset of wavelengths. In step 2705, a set of initialwavelength strings are generated, wherein each wavelength stringcontains indices of wavelengths to be selected. In step 2710, thefitness of calibration models generated using select wavelengths asindicated by each wavelength string is calculated. In step 2715,high-performing wavelength strings from step 2710 are reproduced. Instep 2720, crossover of the wavelength strings reproduced in step 2715is performed. In step 2725, mutation of the wavelength strings producedin step 2720 is performed. In step 2730, steps 2710-2725 are repeated asnecessary or desired. The steps of the method 2700 are described infurther detail in reference to FIGS. 28-32.

FIG. 28 is a schematic illustration of the step 2705 of generatinginitial wavelength strings. A genetic algorithm for wavelength selectioncomprises the step of generating initial wavelength strings 2805, or the“generation 0” strings, of wavelength indices. Each wavelength stringmay comprise a plurality of binary values 2810 for the plurality ofwavelengths in the original sample spectra. For example, if each samplespectrum contains spectral measurements at 1000 wavelengths, eachwavelength string may comprise 1000 binary values. FIG. 28 shows eachwavelength string comprising “n” number of binary values. Each binaryvalue (e.g., 0 or 1) can indicate whether or not the data for thecorresponding wavelength will be used in building the calibration model.For example, a value of “1” corresponding to the wavelength 2640 nm canindicate that the spectral measurement at 2640 nm will be used inbuilding the calibration model, and in generating predictions therefrom;conversely, a value of “0” corresponding to the wavelength 1240 nm canindicate that the spectral measurement at 1240 nm will not be used inbuilding the calibration model or generating predictions therefrom. Thealgorithm may be configured to form the wavelength strings usingpseudo-random number generators. The algorithm may be configured togenerate any number of wavelength strings as appropriate for theoptimization (FIG. 28 shows “k” wavelength strings).

FIG. 29 shows a method 2900 of calculating the fitness of a calibrationmodel generated using selected wavelengths. In step 2905, a calibrationmodel is generated using the selected wavelengths as indicated by awavelength string. For example, the calibration model may be generatedas described in relation to FIG. 22, wherein only spectral data for thewavelengths indicated by the wavelength strings are used. The generatedcalibration model can be used to predict blood pressure from spectraldata of blood samples. In step 2910, the calibration model is used togenerate predicted blood pressure based on spectral data of bloodsamples, wherein the blood samples also have corresponding referenceblood pressure measurements (acquired, for example, using blood pressurecuffs or ambulatory blood pressure monitoring devices). In step 2915,the predicted blood pressure values are compared to the referencemeasurements, and a standard error of prediction (SEP) is calculated,which indicates the fitness of the calibration model. A cross-validationor a true validation procedure may be used in fitting data to the modeland calculating resultant SEP, wherein a cross-validation procedure canuse a set of calibration data with one sample removed from the set ateach iteration, and wherein a true validation can use one or more newsets of data, previously unseen by the calibration model. Individualwavelength strings yielding the best fitness may be selected forreproduction.

FIG. 30 is a schematic illustration of the step 2720 of performingcrossover of wavelength strings. Newly reproduced wavelength strings,selected from the previous generation of wavelength strings based ontheir fitness performance, are mated or paired at random. Each pair 3000a of mated wavelength strings then undergoes crossover, wherein aposition 3005 in the pair of strings is randomly chosen at which thesubsequent bits or values are swapped. As shown in FIG. 30, a portion3015 of the first wavelength string 3010 a of the pair is swapped orcrossed over with a corresponding portion 3025 of the second wavelengthstring 3020 b of the pair, wherein the portions 3015 and 3025 containbinary values of the wavelength strings downstream of the randomlychosen position 3005. In the resultant, crossed over pair 3000 b, thefirst wavelength string 3010 b now contains the portion 3025 originallyfrom the second wavelength string, while the second wavelength string3020 b now contains the portion 3015 originally from the firstwavelength string.

FIG. 31 is a schematic illustration of the step 2725 of performingmutation of wavelength strings. Newly reproduced and subsequentlycrossed over wavelength strings 3100 a undergo mutation, wherein bitsare flipped at a low probability across the set of wavelength strings.As shown in FIG. 31, a bit 3115 a of a first wavelength string 3110 a,or a bit 3125 a of a second wavelength string 3120 a, can be randomlyselected, and the binary value can be flipped (e.g., from 1 to 0 or from0 to 1). The resultant set of wavelengths strings 3100 b now containsthe first wavelength string 3110 b with the mutated bit 3115 b, and thesecond wavelength string 3120 b with the mutated bit 3125 b. Mutation ofthe wavelength strings can help provide and maintain diversity in thestring population, by preventing local minima and keeping the geneticalgorithm searching for the optimal solution. The crossover and mutationsteps can help ensure that new random combinations of wavelengths arecontinually explored.

The steps of fitness calculation, wavelength string reproduction,crossover, and mutation as described herein can be repeated as necessaryor desired. For example, the steps may be repeated for a set number ofgenerations, until a desired level of fitness is obtain, or until thefitness distribution ceases to improve.

While method 2700 describes an exemplary application of a geneticalgorithm to select a parameter in the frequency domain, a geneticalgorithm may also be used to select a parameter in the concentrationdomain. For example, a genetic algorithm may be applied to select theblood pressure measurements to be used as reference values in buildingthe calibration model and in determining the predictive performance ofthe calibration model. It is generally recognized that the measure ofblood pressure most clearly related to patient morbidity is the averagelevel of blood pressure over prolonged periods of time, or “true” meanblood pressure. Therefore, the “true” mean blood pressure measurement ofa subject is ideally used as the reference value in building thecalibration model and generating predictions therefrom. However, it isoften difficult to know which blood pressure measurements (e.g., cuffreadings or ambulatory blood pressure measurements acquired over varioustime periods and/or at various intervals) represent the “true” meanblood pressure of the subject and should be selected as the referencevalue. A genetic algorithm can be used to select the reference valuesthat yield optimal predictive performance of the calibration modelsgenerated therefrom.

FIG. 32 illustrates a method 3200 of applying a genetic algorithm toselect a subset of blood pressure reference values. In step 3205, a setof initial concentration strings are generated, wherein eachconcentration string contains various blood pressure reference values orconcentrations acquired from a subject. Each “bit” in a string cancontain a binary value indicating whether or not the correspondingreference value should be selected. In step 3210, the fitness ofcalibration models generated using selected reference values asindicated by each concentration string is calculated. Cross-validatingthe generated calibration models against the same calibration data setcan lead to overfitting the selected reference values to the calibrationdata. Overfitted reference values can lead to poor predictiveperformance of the calibration model when the model is applied to newdata. Therefore, in this application, a true validation procedure ispreferably used instead of a cross-validation procedure to determine thefitness values (e.g., standard error of prediction). In step 3215,high-performing concentration strings from step 3210 are reproduced. Instep 3220, crossover of the concentration strings reproduced in step3215 is performed. In step 3225, mutation of the concentration stringsproduced in step 3220 is performed. In step 3230, steps 3210-3225 arerepeated as necessary or appropriate. The steps of the method 3200 canbe similar in many aspects to the steps of methods 2700, described indetail in reference to FIGS. 28-31.

A genetic algorithm may also be applied to select a parameter in thetemporal domain. The methods of measuring blood biomarkers as describedherein may comprise various parameters associated with time (e.g, lengthof time of a sample processing/treatment step, length of time of samplestorage before measurement, sampling intervals, time of day of sampleacquisition and/or measurement, etc.). For example, as described herein,spectroscopic data of red blood cells may be acquired over a time periodof a gradual drying process, wherein the drying process may affect thechemical composition, protein structure, and/or conformation of the redblood cell membrane over time. Accordingly, measurements taken atvarious time points during the drying process may yield differentspectral data, wherein some time points may be ideally suited forobtaining spectral data that is highly predictive of blood pressurelevels. A genetic algorithm may be applied to select the lengths of timeof the sample drying process that yield spectral data optimally suitedfor the prediction of blood pressure levels.

FIG. 33 illustrates a method 3300 of applying a genetic algorithm toselect a subset of sample drying time points. In step 3305, a set ofinitial time strings are generated, wherein each time string maycontains various sample drying time points at which sample data isacquired. Each “bit” in a string can contain a binary value indicatingwhether or not the spectral data acquired at the corresponding timepoint is to be used for predicting blood pressure. In step 3310, fitnessis calculated for the predictions generated using sample data at timepoints as indicated by each time string. In step 3315, high-performingtime strings from step 3310 are reproduced. In step 3320, crossover ofthe time strings reproduced in step 3315 is performed. In step 3325,mutation of the time strings produced in step 3320 is performed. In step3330, steps 3310-3325 are repeated as necessary or appropriate. Thesteps of the method 3300 can be similar in many aspects to the steps ofmethods 2700, described in detail in reference to FIGS. 28-31.

The steps of methods 2700, 3200, and 3300 are provided as examples ofapplying a genetic algorithm to optimize the selection of a parameter. Aperson of ordinary skill in the art will recognize many variations andmodifications of methods 2700, 3200, and 3300 based on the disclosureprovided herein. For example, some steps may be added or removed. Someof the steps may comprise sub-steps. Many of the steps may be repeatedas many times as appropriate or necessary. One or more steps may beperformed in a different order than as illustrated in FIGS. 27, 32, and33.

EXPERIMENTAL

Based on the teachings disclosed herein, a person of ordinary skill inthe art can identify biomarkers in blood in order to determine thepresence of hypertension. A person of ordinary skill in the art canconduct experiments to identify one or more additional biomarkers inorder to predict current and/or recent central aortic vessel pressures.

The apparatus can be constructed as described herein to measure the oneor more biomarkers of the blood sample. A population of subjects can bemeasured with the apparatus to determine the presence of biomarkers andthis data can be compared with measured blood pressure of the subjects.The relationship among the one or more biomarkers and blood pressure canbe determined with one or more analytic models as described herein. Forexample, the high blood pressure of the subject can be identified inresponse to the amount of biomarker measured, and the high bloodpressure can be presented to the physician as one or more of an index ora scale. In many embodiments, the amount of biomarker can be mapped to atraditional systolic blood pressure with a mapping function such as alook up table or scaling factor. For example, the systolic bloodpressure can be determined with a linear function such as

BLOOD PRESSURE=A*[CONCENTRATION OF BIOMARKER]+B

where the BLOOD PRESSURE is the determined blood pressure in mm Hg inresponse to the CONCENTRATION OF BIOMARKER in ng/ml times the scalingconstant A plus the offset constant B. The parameters A and B can bedetermined based on the study population, for example.

FIG. 13 shows a commercially available spectroscopy apparatus 1300suitable for combination in accordance with embodiments. Thecommercially available spectroscopy apparatus may comprise an ALPHA-Pspectrometer, and may comprise an evanescent wave FT-IR spectrometer forexample. The commercially available evanescent wave spectrometer can beused to measure one or more model substances 1310 such as chicken redblood cells, fresh, or treated with gluteraldehyde to stiffen themembrane, for example.

FIG. 14 shows example spectra of fat 1400, milk 1410, dried red bloodcells 1420, red blood cells 1430, red meat 1440, and red wine 1450.

FIG. 15 shows an aspirin study. The aspirin study shows principalcomponent analysis components eigenvector 1 1510, eigenvector 2 1520,and eigenvector 3 1530. Aspirin study shows a human subject's responseto a baby aspirin. The study used the first 10 spectra from each dataset. PCA shows a difference in the blood sample without aspirin 1500 andblood sample with aspirin 1505. The first factor 1510 corresponds tointensity differences in the signal. The second factor 1520 correspondsto a change because of the shift in the Amide II peak (positive for theno aspirin samples and negative for the aspirin samples).

FIG. 16A shows multivariate curve resolution (MCR) factors. Factor 1 maycomprise spectral peaks such as one or more of a carboxylate peak 1600,a CH3 bending peak 1605, an Amide II peak 1610, or an Amide I peak 1615,for example. Factor 3 may comprise Amide I, Amide II broadening 1620,for example. Factor 4 may comprise a water peak 1625, for example, at1560 cm⁻¹ (inverse centimeters). Factor 5 may comprise a 1560 cm⁻¹shift. Factor 6 may comprise a baseline offset, for example. Manyadditional factors can be used in accordance with the embodimentsdescribed herein, for example.

FIG. 16B shows MCR concentrations for the factors of FIG. 16A forchicken blood preliminary results as follows:

1-5: Fresh Supernatant

6-10: Gluteraldehyde Supernatant

9-20: Fresh Cells, 3 replicates at each settling time (time 0: F1, F3,F5; time 1: F1, F3, F5; time 2: F1, F3, F5; time 3: F1, F3, F5)

21-32: Glut Cells, 3 replicates at each settling time (time 0: G2, G4,G6; time 1: G2, G4, G6; time 2: G2, G4, G6; time 3: G2, G4, G6)

These preliminary data show concentration differences among the samplesin accordance with embodiments described herein.

The analysis may comprise one or more analysis tools of commerciallyavailable software such Chemometrics metrics software available fromEigenvector Research Incorporated, for example as listed on with WorldWide Web (www.eigenvector.com/software/solo.htm). The software maycomprise one or more of the following capabilities:

-   -   Data Exploration and Pattern Recognition (Principal Components        Analysis (PCA), Parallel Factor Analysis (PARAFAC), Multiway        PCA)    -   Classification (soft independent modeling of class analogies        (SIMCA), k-nearest neighbors, Partial Least Squares (PLS)        Discriminant Analysis, Support Vector Machine Classification,        Clustering (Hierarchical Cluster Analysis, HCA))    -   Linear and Non-Linear Regression (PLS, Principal Components        Regression (PCR), Multiple Linear Regression (MLR), Classical        Least Squares (CLS), Support Vector Machine Regression, N-way        PLS, Locally Weighted Regression)    -   Self-modeling Curve Resolution, Pure Variable Methods        (Multivariate Curve Resolution (MCR), Purity (compare to        SIMPLSMA), CODA_DW, CompareLCMS)    -   Curve fitting and Distribution fitting and analysis tools    -   Instrument Standardization (Piece-wise Direct, Windowed        Piece-wise, OSC, Generalized Least Squares Preprocessing)    -   Advanced Graphical Data Set Editing and Visualization Tools    -   Advanced Customizable Order-Specific Preprocessing (Centering,        Scaling, Smoothing, Derivatizing, Transformations, Baselining)    -   Missing Data Support (Singular Value Decomposition (SVD) and        Non-Linear Iterative Partial Least Squares (NIPALS))    -   Variable Selection (Genetic algorithms, Iterative PLS (IPLS),        Selectivity, Variable Importance Projection (VIP))

FIG. 17 shows results from a study with gluteraldehyde-treated red bloodcells. A scatter plot of MCR Factor 3 in relation to Factor 4 is shown.The data are shown for combined settling times of 2, 4, and 6 minutes.Membrane secondary structural changes can be induced by brief treatmentwith gluteraldehyde. Washed intact chicken red blood cells wereobtained, some fresh and some treated briefly with gluteraldehyde.Membrane secondary structural changes are clearly visible based on thecomparison of Factor 3 and Factor 4.

Gluteraldehyde induces structural changes in the red blood cell membraneand is capable of denaturing proteins. Without being bound by anyparticular theory, the spectral changes induced by gluteraldehyde canhave at least some similarity to spectral changes induced by bloodpressure of the subject. For example, the red blood cell of thehypertensive subject can be more deformable than a subject having normalblood pressure. Gluteraldehyde is a cross-linking molecule that affectsthe structural rigidity of the red blood cell membrane.

FIG. 18 shows results from a study with human blood and aspirin. Wholeblood from one volunteer was obtained via fingerstick before and afterthe ingestion of acetylsalicylic acid (ASA, aspirin). Aspirin inducesmembrane structural changes in the red blood cell. A drop of heparinizedblood was measured directly on a horizontal sampler and spectra wereacquired while allowing the red blood cells to gravimetrically separatefrom whole blood and deposit onto the sampler window. This was done toallow chemometric separation of the pure membrane spectrum. Data wereanalyzed using multivariate curve resolution (MCR). This experiment wasrepeated 4 times on 4 separate days, and the data set consists of 80full infrared spectra. The data for MCR factor 6 and factor 10 show aclear separation between red blood cell membrane before and afteringestion of aspirin. Results are consistent across all 4 study days.

FIG. 19 shows MCR factors 3, 6, and 10, in accordance with embodiments.Factor 3 may correspond to the protein structure of blood. Factor 3 canbe used as a reference for two or more factors that allow discriminationof blood after an oral dose of aspirin. Factor 6 may correspond to ashift in the Amide I peak. Factor 10 may correspond to a shift in AmideII, for example.

FIG. 20 shows a 3D plot of results from a study of the effect ofgluteraldehyde on blood.

FIG. 21 shows a 2D plot of the data of FIG. 20. Factor 3 represents theprotein structure of the blood and is used as a reference for factors 6and 10. Factor 6 predominantly exhibits a shift in Amide I. Factor 10predominantly exhibits a shift in Amide II.

Spectra were taken on an untreated blood sample and the last tenequilibrated spectra 2000 were selected for use in the further analysis.Blood was treated with gluteraldehyde and spectra were taken, with thelast ten equilibrated spectra 2010 being selected for use in the furtheranalysis. Spectral data were normalized to the Amide I peak 1610.Changes from after the gluteraldehyde treatment include a small shift inthe Amide I peak, a larger shift in the Amide II peak 1615, a change inCarboxylate peak 1600 intensity, and an increase in Amide III band 1630.

FIG. 23 shows results from a study of mean arterial blood pressure (MAP)measurements in human subjects using a sphygmomanometer or bloodpressure cuff. Blood pressure was monitored in 11 subjects, 8 havinghigh blood pressure (systolic 140-170 mmHg/diastolic 90-120 mmHg) and 3having normal to low blood pressure, over a period of 28 days. Subjectswere trained in the use of an ambulatory blood pressure (ABP) monitoringdevice (Welch Allyn ABPM6100 Blood Pressure Monitor), designed for24-hour blood pressure monitoring. Subjects recorded blood pressurereadings once every day for 5 days of a week, and 6 times a day for 2days of a week. FIG. 23 shows the MAP values (mmHg) averaged persubject, wherein the MAP values were calculated from the systolic (SP)and diastolic (DP) cuff measurements using the equationMAP=((2×DP)+SP)/3. In FIG. 23, each vertical “line” of data pointsrepresents MAP measurements for a single subject, and the sloped linegoing through all of the vertical “lines” of data points shows theaverage of the MAP measurements for each subject over the 28-day studyperiod. The data shows the wide variation in the cuff measurements foreach subject over the study period. Any one of the data points in eachvertical “line” of data points may represent a single cuff measurementtaken from a patient, and as shown in FIG. 23, a single cuff measurementmay be significantly different from the average MAP value for thepatient over the 28-day study period (data point through which thesloped line extends). It is generally recognized that the average levelof blood pressure over prolonged periods of time represents the measureof blood pressure that is most clearly related to morbid events inpatients. However, clinic measurement often comprise single cuffreadings taken in the office, and ambulatory blood pressure (ABP)monitoring are not widely used because the devices may be cumbersome andinconvenient for patients. The results of the study in FIG. 23 show thatsingle cuff readings can often be inaccurate in determining thepatient's true average blood pressure over a prolonged period of time.

In parallel with blood pressure cuff measurements, the bloodspectroscopic measurement apparatus and methods as described herein wereused to analyze blood samples of the same 11 subjects as in the study ofFIG. 23. Blood samples were drawn once a week over the 28-day studyperiod, for a total of 5 blood samples per subject over the course ofthe study. The blood samples were analyzed as described herein using TIRspectroscopy to measure changes in the membrane of the red blood cellsin the blood samples. Subsequently, spectroscopic data was analyzedusing Augmented Classical Least Squares methods as described herein. Theanalyzed spectroscopic data was converted to predicted average bloodpressure values (mmHg) using a prediction function as described herein.

FIG. 26 shows the pure component spectrum of red blood cells obtainedfrom the spectroscopic data generated from the study. Such a purecomponent spectrum may be obtained using the spectral analysis methodsas described herein. The labels indicate spectral bands that are seen tovary with changes in blood pressure, for example by broadening and/orshifting. Such spectral changes indicative of blood pressure changeinclude shifts in the amide bands including the Amide I peak 2610, AmideII peak 2620, and Amide III peak 2630. The shifts in the amide bands mayindicate changes in the protein secondary structure, related to changesin blood pressure. Blood pressure-related spectral changes may furtherinclude changes in the carboxylate region 2600. A transition metalcarbonyl band 2640 at around 1970 cm⁻¹, shown in the inset at 100× scaleexpansion, can also be seen to vary with blood pressure. The band 2640may be attributed to a transition metal carbonyl bond, which may resultfrom a hemoglobin-spectrin complex.

FIGS. 24A and 24B show results from a study of average blood pressuremeasurements in human subjects, using a measurement apparatus inaccordance with embodiments. FIG. 24A shows the predicted mean arterialpressure (MAP) values (mmHg) derived from spectroscopic measurements ofblood samples for each subject over the 28-day study period, such thateach vertical “line” of data points represents predicted MAP values fromblood measurements from a single subject. The predicted MAP values aregraphed against reference MAP measurements, derived from cuffmeasurements as described for FIG. 23. The sloped line going through thedata points shows the average of the predicted MAP values for eachsubject over the 28-day study period. FIG. 24A show that the predictedaverage MAP values derived from blood spectroscopic measurements areable to predict the average blood pressure (MAP) of a subject with astandard error of about 11.7 mmHg, and a coefficient of determination(R²) of about 0.7.

FIG. 24B shows the predicted systolic blood pressure (SP) values (mmHg)derived from spectroscopic measurements of blood samples for eachsubject over the 28-day study period, such that each vertical “line” ofdata points represents predicted SP values from blood measurements froma single subject. The predicted SP values are graphed against referenceSP measurements, derived from cuff measurements as described for FIG.23. The sloped line going through the data points shows the average ofthe predicted SP values for each subject over the 28-day study period.FIG. 24B show that the predicted average SP values derived from bloodspectroscopic measurements are able to predict the average bloodpressure (SP) of a subject with a standard error of about 17 mmHg, and acoefficient of determination (R²) of about 0.7. Each of the predictionresults as shown in FIG. 24B was produced using a full calibrationmodel, built from the complete spectral data of all measured bloodsamples including the spectral data of the predicted data point. Inorder to cross-validate the prediction results, each predicted bloodpressure value was also calculated using a cross-validation calibrationmodel, built from spectral data excluding the data of the predicted datapoint. The cross-validation procedure yielded prediction results similarto the results shown in FIG. 24B, wherein a positive correlation wasobserved between predicted blood pressure (SP) values and reference SPmeasurements, with a standard error of prediction of about 23 mmHg. Thewithin-subject precision between the 5 blood measurements from a singlesubject, made over the 28-day study period, was also determined. Theprecision across all subjects was determined to be about 13 mmHg whenusing the full calibration model, and about 19 mmHg when using thecross-validation calibration model.

Comparing the results of FIGS. 24A and 24B with the results of FIG. 23,it can be seen that a single blood spectroscopic measurement,represented by a single data point in each vertical “line” of datapoints, can more closely predict the average blood pressure value for apatient over the 28-day study period (data point through which thesloped line extends) than a single cuff reading, represented by a singledata point in each vertical “line” of data points in FIG. 23. Theaccuracy of predicting average blood pressure values using bloodspectroscopic measurements may be further improved by appropriatemodifications to the measurement apparatus and/or data analysisalgorithms. While FIGS. 24A and 24B show MAP and SP values,respectively, the spectroscopic data can be converted to diastolic bloodpressure, pulse pressure, or any other clinically relevant measure ofblood pressure. It is noted that for the results of one of the subjectsshown in FIG. 24, in-clinic mercury sphygmomanometer measurements werefor substituted for the subject-provided ABP measurements in derivingthe reference blood pressure measurements, because the ABP monitoringdevice used by the subject was found to be functioning improperly duringthe course of the study.

FIGS. 25A and 25B show additional results from the study of FIGS. 24Aand 24B. As described previously herein, the study was conducted in 11human subjects, 8 having high blood pressure (systolic 140-170mmHg/diastolic 90-120 mmHg; “hypertensive”) and 3 having normal to lowblood pressure (“normal”). The predicted mean arterial pressure (MAP) orsystolic pressure (SP) values, derived from blood sample spectroscopicmeasurements, were averaged per subject group (“normal” and“hypertensive”). In FIG. 25A, the center line of each box plotrepresents the median of the MAP values for each subject group, whilethe top and bottom of the boxes represent 25^(th) and 75^(th)percentiles. FIG. 25A shows that for predicted MAP values obtained fromblood spectroscopic measurements, the median MAP of the “normal” subjectgroup is found to be statistically different from the “hypertensive”subject group, with 95% confidence. Similarly, in FIG. 25B, the medianSP value of each group of subjects in shown, along with error barsdisplaying the 95% confidence interval (p<0.05) about the median values.As shown, all subjects can be correctly classified as “normal” or“hypertensive” with 95% confidence. While FIG. 25B shows resultsproduced using a full calibration model, similar results were alsoproduced using a cross-validation calibration model as described herein.

Work in relation with embodiments suggests that the methods andapparatus as described herein may be well-suited for the measurement ofblood samples that have been stored up to about 3 days after collection.No significant changes in spectroscopic data of blood samples wereobserved during such a time window, when red blood cells were purified,washed, and stored under appropriate refrigeration conditions.

FIG. 34 shows the results of a genetic algorithm optimization ofwavelength selection for blood spectroscopic analysis. A geneticalgorithm was applied to the results of the study of FIGS. 24A and 24B,to select a subset of wavelengths or frequencies of the sample spectrato use in building the calibration model or prediction function, asdescribed herein. 208 wavelength strings were generated, each stringcomprising wavelength indices to use during the wavelength selectionprocess. The fitness of each generation of wavelength strings wasdetermined by calculating the cross-validated standard error ofprediction (CVSEP) for the calibration models generated using thespectral data at the selected wavelengths. 20 generations of wavelengthstrings were produced using the genetic algorithm, each generation afterthe first generation reproduced from the highest performing strings ofthe previous generation. As shown in FIG. 34, the performance of thefitness function improved with each subsequent generation of wavelengthstrings, wherein the generation 0 strings yielded an average CVSEP ofabout 24, and the generation 20 strings yielded an average CVSEP ofabout 14.

FIG. 35 shows the final selection of wavelengths of a red blood cellspectrum, optimized using the genetic algorithm procedure of FIG. 34.The original spectrum contained data points at 809 wavelengths orfrequencies. Using the genetic algorithm, 364 wavelengths were selectedto be used in building the calibration model and generation predictedblood pressures from the calibration model. The 364 selected wavelengthswere identified from generation 19 wavelength strings. Some of thewavelengths most consistently identified as important for the predictionof blood pressure included wavelengths of about 1950 to about 2000 cm⁻¹,which can contain a transition metal carbonyl band potentiallyindicative of the formation of a spectrin-hemoglobin complex, linked tothe rigidity of the red blood cell membrane.

FIG. 36 shows the predicted systolic blood pressure (SP) values (mmHg)derived from spectroscopic measurements of blood samples from the studyof FIGS. 24A and 24B, wherein wavelengths selected using a geneticalgorithm were used to generate the predicted values. Each vertical“line” of data points represents predicted SP values from bloodmeasurements from a single subject. The predicted SP values are graphedagainst reference SP measurements, derived from cuff measurements asdescribed for FIG. 23. The sloped line going through the data points isthe line of identity, wherein values lying on the line of identity wouldindicate a perfect prediction or no prediction error. FIG. 36 shows thatthe predicted average SP values derived from blood spectroscopicmeasurements are able to predict the average blood pressure (SP) of asubject with a standard error of about 12.7 mmHg, and a correlationcoefficient (R²) of about 0.8. Compared to the predicted valuesgenerated from the full set of wavelengths, predicted values generatedfrom the optimized subset of wavelengths show clear improvement inpredictive performance. FIG. 24B indicates a standard error ofcalibration (SEC) of about 16.7 mmHg (R²≈0.7) for the predictionsgenerated from the full set of wavelengths, compared to across-validated standard error of prediction (CVSEP) of about 12.7 mmHgfor the predictions generated from the selected subset of wavelengths,as shown in FIG. 36. For SEC calculations, the predicted spectra notremoved during the calibration building process, which often leads tomore optimistic results compared to a cross-validated procedure. Whilenot shown in FIG. 24B, the CVSEP of the results presented in FIG. 24Bwas approximately 23.4 mmHg (R²≈0.5). The reduction of CVSEP from about23.4 mmHg in the results of FIG. 24B to about 12.7 mmHg in the resultsof FIG. 36 indicates a substantial improvement in predictive performancedue to wavelength selection.

Spontaneously Hypertensive Mouse and Rat Studies

Work in relation to embodiments suggests that animal models can be usedto identify biomarkers suitable for use in humans. Vertebrateerythrocytes consist mainly of hemoglobin. The mammalian red blood cellcomprises similar structures, proteins, and biomarkers among manyspecies including mammals such as humans, rats, and mice. Mammalianerythrocytes typically have a biconcave disk shape, which optimizestheir flow properties in larger vessels. Generally, mammalianerythrocytes are flexible and deformable to enable passage through smallcapillaries.

Mammalian erythrocytes are non-nucleated in their mature form, and alsolack all other cellular organelles. Consequently, they lack DNA andcannot synthesize RNA. Structural properties are linked to the membrane.The membrane comprises a lipid bilayer, membrane proteins, lipids, andcarbohydrates. The membrane is composed of three layers: the outer,carbohydrate-rich glycocalyx, the lipid bilayer, and the membraneskeleton. Mammalian erythrocyte lipid bilayers contain similarcompositions of phospholipids, including choline phospholipids (CPs),acidic phospholipids (APs), and phosphatidylethanolamine (PE).

Spontaneously hypertensive rats, and similar model mice, compriseattributes that can be suitable for identification of blood markers ofhealth of humans, in accordance with embodiments.

High-density lipoprotein (HDL) and low-density lipoprotein (LDL) areboth present in humans, mice, and rats. Wild-type mice are usuallyresistant to lesion development and clear LDL very quickly. Mouse modelsmore useful for comparison to humans have been developed. For example,low-density lipoprotein receptor-deficient mice (LDLR −/− mice) andapolipoprotein E-deficient mice (apoE −/− mice) are widely used. LDLR−/− mice respond effectively to peroxisome proliferator-activatedreceptor (PPAR) agonists, which are used in humans as well to reducetriglycerides (TG) and LDL cholesterol and to raise HDL cholesterol.ApoE −/− mice develop extensive atherosclerotic lesions, and respond totreatment with statins and PPAR agonists, as do humans.

The spontaneously hypertensive rat (SHR) is another animal model ofprimary hypertension commonly used to study cardiovascular disease.Around 5-6 weeks of age, the SHR begins hypertensive development. Inadult age, systolic pressures reach 180-200 mmHg. Around 40-50 weeks,the SHR typically develops characteristics of cardiovascular disease,such as vascular and cardiac hypertrophy. Similar models have beendeveloped in mice, such as JAX BPL/2 mice. BPL/2 mice develop elevatedsystolic blood pressure at five weeks of age, and by 150 days of ageshow an average blood pressure of 119 mmHg. This predictable progressionallows longitudinal studies of the same population both before and afterhypertensive development. Such studies can show biomarker levels andother changes associated with the onset of hypertension and/or theimpacts of hypertension.

With the teachings of the present disclosure, a person of ordinary skillin the art can conduct experiments to measure and identify blood basedbiomarkers to determine the health of a human subject without undueexperimentation.

Reference is made to the following claims which recite combinations thatare part of the present disclosure, including combinations recited bymultiple dependent claims dependent upon multiple dependent claims,which combinations will be understood by a person of ordinary skill inthe art and are part of the present disclosure.

While preferred embodiments of the present disclosure have been shownand described herein, it will be obvious to those skilled in the artthat such embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will be apparent to those skilledin the art without departing from the scope of the present disclosure.It should be understood that various alternatives to the embodiments ofthe present disclosure described herein may be employed withoutdeparting from the scope of the present invention. Therefore, the scopeof the present invention shall be defined solely by the scope of theappended claims and the equivalents thereof

We claim:
 1. An apparatus to identify a marker from blood of a subject,comprising: a processor comprising instructions to identify a biomarkerof a blood sample of the subject.
 2. An apparatus as in claim 1 whereinthe marker comprises a plaque biomarker.
 3. An apparatus as in claim 2wherein the plaque biomarker comprises a material of one or more of afoam-cell rich plaque, a lipid-rich plaque, or a collagen-rich plaque.4. An apparatus as in claim 1 wherein the marker comprises a bloodpressure biomarker.
 5. An apparatus as in claim 4, wherein the processorcomprises instructions to identify the high blood pressure biomarker inresponse to an evanescent wave measurement of the sample on ameasurement surface.
 6. An apparatus as in claim 4, wherein theprocessor comprises instructions to identify the high blood pressurebiomarker in response to a transmission measurement of the samplebetween the measurement surface and an opposing surface.
 7. An apparatusas in claim 4, wherein the processor is configured to identify thebiomarker in response to the blood sample comprising one or more of redblood cells, serum or plasma and wherein the apparatus is configured tomeasure a first component of the sample comprising an increased amountof red blood cells in relation to the sample and a second component ofthe sample comprising a decreased amount of red blood cells in relationto the sample.
 8. An apparatus as in claim 7, wherein the processor isconfigured to measure the first component of the blood sample with oneor more of a first evanescent wave measurement or a first opticaltransmission measurement and to measure the second component with one ormore of second evanescent wave measurement or a second opticaltransmission measurement.
 9. An apparatus as in claim 8, wherein theprocessor is configured to measure the first component of the bloodsample with the first evanescent wave measurement and the first opticaltransmission measurement and to measure the second component with thesecond evanescent wave measurement and the second optical transmissionmeasurement.
 10. An apparatus as in claim 4, further comprising: anoptically transmissive material having a measurement surface to receivethe blood sample; one or more optical energy detectors coupled to theprocessor and optically coupled to the surface to measure an amount ofthe biomarker on the measurement surface.
 11. An apparatus as in claim10, further comprising: one or more light sources to generate one ormore measurement light beams; one or more optical elements arranged todirect the one or more measurement light beams from the one or morelight sources to the surface and to direct light from the measurementsurface to the one or more optical energy detectors; wherein the one ormore optical elements is configured to measure a surface of one or morered blood cells on the measurement surface with an evanescent waveextending from the measurement surface to the one or more red bloodcells on the measurement surface and wherein the biomarker comprises abiomarker of a membrane of a red blood cell.
 12. An apparatus as inclaim 11, wherein the apparatus is configured to measure the one or morered blood cells in the presence of an anticoagulant in order to permit aplurality of red blood cells to contact the surface with each of saidplurality of red blood cells having an elongate surface extending alongan elongate dimension aligned with the surface in order to measure amembrane of said each of the plurality of red blood cells.
 13. Anapparatus as in claim 10, further comprising: a prism comprising theoptically transmissive material, the prism comprising the measurementsurface to receive the blood sample.
 14. An apparatus as in claim 13wherein the prism comprises a Dove prism and wherein the Dove prism isilluminated at a first angle with a first light beam to measure one ormore red blood cells on the measurement surface with the evanescent waveof light internally reflected from the measurement surface and whereinthe Dove prism is illuminated with a second light beam at a second angleto measure light transmitted through the measurement surface and theblood sample.
 15. An apparatus as in claim 14, wherein the Dove prismcomprises an elongate axis and a width transverse to the elongate axisand wherein the axis and the measurement surface extend in a firstdirection and the width extends in a second direction transverse to thefirst direction.
 16. An apparatus as in claim 15, wherein the Dove prismcomprises a first inclined surface extending at a first angle oblique tothe measurement surface, and a second inclined surface extending asecond angle oblique to the measurement surface and wherein the lightbeam illuminates the surface with total internal reflection to providethe evanescent wave and the light from the evanescent wave istransmitted through the second inclined surface.
 17. An apparatus as inclaim 16, wherein the measurement surface is located between the firstinclined surface and the second inclined surface and wherein an opposingsurface extends between the first inclined surface and the secondinclined surface opposite the measurement surface and wherein atransmission measurement light beam is transmitted through the opposingsurface and the measurement surface to measure transmission through thesample.
 18. An apparatus as in claim 4, the processor comprisesinstructions pressurize the sample and to identify the blood pressurebiomarker based on one or more temporal denaturation profiles inresponse to pressurization of a sample chamber and wherein the opticallytransmissive substrate is dimensioned to pressurize the blood sample.19. An apparatus as in claim 4, wherein the one or more light sourcesand the one or more detectors and the optics are configured to measurechemicals based on optical spectroscopy that are correlated withpresence of hypertension and/or correlated with level of blood pressure.20. An apparatus as in claim 4, wherein the processor comprises one ormore instructions to one or more of transform or calibrate biomarkerconcentration data into a corresponding scale related a systolic and adiastolic pressure during the cardiac cycle, such that the biomarkerconcentration is transformed from a concentration to a blood pressure inresponse to measured data of a plurality of subjects.
 21. An apparatusas in claim 4, wherein the blood pressure biomarker comprises one ormore of adenosine triphosphate, or, one or more transmembrane proteins,one or more proteins of the membrane skeleton, one or more lipids of thered blood cell membrane, a relative ratio of the one or more lipids ofthe red blood cell membrane, or biomaterial deposited on the surface ofthe red blood cell membrane.
 22. An apparatus as in claim 1 wherein thebiomarker comprises a spectral signal and wherein the instructionscomprise instructions to identify the biomarker in response to thespectral signal.
 23. An apparatus as in claim 1 wherein the biomarkercomprises a plurality of biomarkers comprising a spectral signal andwherein the instructions comprise instructions to identify the pluralityof biomarkers in response to the spectral signal.
 24. An apparatus as inclaim 1 wherein the biomarker comprises one or more of a protein, alipid, a high density lipoprotein, a low density lipoprotein, membraneprotein, a trans membrane protein, a lipid, or a spectrin network andwherein spectra of the biomarker comprise one or more of an Amide Ipeak, an Amide II peak, a Carboxylate peak, or an Amide III band.
 25. Anapparatus as in claim 24 and wherein the Amide I peak, the Amide IIpeak, and the Amide III band correspond to an alpha helix of thebiomarker, a beta sheet of the biomarker, and disordered protein of thebiomarker, respectively.
 26. An apparatus as in claim 25 wherein theinstructions comprise instructions to identify the biomarker in responseto one or more of the Amide I peak, the Amide II peak, the Carboxylatepeak, or the Amide III band.
 27. An apparatus as in claim 1 wherein thebiomarker comprises a spectral signal and wherein instructions compriseinstructions to determine a plurality of factors in response to thespectral signal.
 28. An apparatus as in claim 27 wherein the pluralityof factors comprises a plurality of functions.
 29. An apparatus as inclaim 27 wherein the plurality of factors comprises a plurality offactors of a neural network.
 30. An apparatus as in claim 27 wherein theplurality of factors comprises a plurality of at least four factors toidentify the biomarker.
 31. An apparatus as in claim 27 wherein theplurality of factors comprises a plurality of at least ten factors toidentify the biomarker.
 32. An apparatus as in claim 27 wherein theplurality of factors comprises a plurality of at least ten orthogonalfactors to identify the biomarker.
 33. An apparatus as in claim 32wherein the plurality of factors comprises a plurality of multivariatecurve resolution factors or a plurality of multi-component analysisfactors.
 34. An apparatus as in claim 27 wherein the instructionscomprise instructions to identify the biomarker in response to arelationship among a plurality of factors determined in response to thespectral signal.
 35. An apparatus as in claim 1 wherein the biomarkercomprises a red blood cell membrane marker responsive to aspirin.
 36. Anapparatus as in claim 1 wherein the biomarker comprises a red blood cellmembrane marker responsive to gluteraldehyde.
 37. An apparatus toidentify a biomarker of a cell membrane of a subject, comprising: aprocessor comprising instructions to identify the biomarker of a thecell membrane of the subject.
 38. A method of identifying a biomarker ofa subject, the method comprising: identifying a biomarker of a cellmembrane of the subject.
 39. A method of identifying high blood pressureof a subject, the method comprising: identifying a blood pressurebiomarker of a blood sample of the subject.
 40. A method as in claim 39,the biomarker has been correlated with high blood pressure in order toquantify the high blood pressure.
 41. A method as in claim 39, furthercomprising using the apparatus as in any one of the preceding claims.42. A method of assessing risk of cardiovascular disease of a subject,the method comprising, measuring a spectrum of one or more red bloodcells; and determining a risk of cardiovascular disease in response tothe spectrum.
 43. A method as in claim 42, wherein the spectrumcomprises an evanescent wave spectrum.
 44. A method as in claim 42,wherein the risk is determined without in vitro enzymatic analysis. 45.A method as in claim 42, wherein the risk is determined without lysingthe red blood cells.
 46. A method as in claim 42, wherein the risk isdetermined without or pretreating a sample comprising the one or morered blood cells.
 47. A method as in claim 42, wherein the spectrumcomprises a plurality of spectra.
 48. An apparatus to identify abiomarker of a subject, the apparatus comprising: a sample container toreceive a blood sample of the subject; and a spectrometer comprising oneor more optics to couple to the container.
 49. An apparatus as in claim48, wherein the sample container extends in a vertical direction adistance sufficient to gravimetrically separate red blood cells fromserum of the blood sample.
 50. An apparatus as in claim 49, wherein thesample container extends in a vertical direction a distance sufficientto wash the red blood cells when the red blood cells separate from theserum and wherein the container comprises a volume of a solutionsufficient to wash the red blood cells.
 51. An apparatus as in claim 48,wherein the sample container comprises a solution having a density lessthan a density of red blood cells in order to separate gravimetricallyseparate the red blood cells from the plasma.
 52. An apparatus as inclaim 51, wherein the solution comprises a spectroscopic referencemarker in order to one or more of calibrate the spectrometer or identifyspectral shifts in relation to the reference marker.
 53. An apparatus asin claim 48, wherein the sample container comprises a column extendingin a vertical direction.
 54. An apparatus as in claim 48, wherein thesample container comprises a removable container comprising a waveguideto introduce an evanescent wave into a lower portion of the container.55. An apparatus as in claim 54, wherein the waveguide comprisesGermanium.
 56. An apparatus as in claim 54, wherein the waveguidecomprises a lower end of the container to support the solution andreceive red blood cells near an upper surface of the waveguide.
 57. Anapparatus as in claim 56, wherein an upper surface of the waveguide isconfigured to contact the solution and receive a red blood cell on theupper surface.
 58. An apparatus as in claim 56, wherein an upper surfaceof the waveguide and the lower surface of the container are locatedwithin a 1/e intensity distance of the evanescent wave in order tomeasure red blood cells on the lower surface of the container.
 59. Anapparatus as in claim 54, wherein the spectrometer comprises couplingoptics and a support fixed in relation to the coupling optics, thesupport configured to support the removable container, wherein thesupport and the coupling optics are arranged to align the waveguide withthe coupling optics when the container is supported with the support.60. An apparatus as in claim 59, wherein the coupling optics comprise aninput optic to direct light into the waveguide and an output optic toreceive light from the waveguide and wherein the support is fixed to thespectrometer and sized and shaped in order to align the input optic witha first end of the waveguide and the output optic with a second end ofthe waveguide when the container rests on the support.
 61. An apparatusas in claim 48, wherein the sample container comprises a removablesample container having membrane on a bottom of the container andwherein the waveguide comprises an upper surface to support the membraneand wherein the membrane comprises a thickness of no more than a 1/edistance of the evanescent wave from the waveguide in order to measurered blood cells through the membrane with the evanescent wave.
 62. Anapparatus as in claim 48, wherein the waveguide comprises one or moreprisms.
 63. An apparatus as in claim 48, wherein the waveguide comprisesa first flat surface on a first side and a second flat surface on asecond side opposite the first side and a first optically coupling endto transmit light between the first flat surface and the second flatsurface and a second end optically coupling end to transmit lightreflected between the first flat surface and the second flat surface toa detector.
 64. An apparatus as in claim 63, wherein the waveguidecomprises thickness extending between the first surface and a secondsurface a length extending in a direction of propagation of the lightenergy between the first end and the second end and a width transverseto the length, the width greater than the thickness and the lengthgreater than the thickness such that the light energy reflects aplurality of times from the first surface in order to increase a signalfrom the biomarker.
 65. A method, the method comprising: providing anevanescent wave measurement surface on a lower end portion of acontainer, the container comprising a solution; placing a blood samplein the solution, wherein red blood cells of the sample settle withgravimetric separation onto the evanescent wave measurement surface; andmeasuring spectra of the red blood cells on the evanescent wavemeasurement surface.
 66. A method as in claim 65, wherein the red bloodcells comprise a plurality of red blood cells having a long dimensionaligned with the evanescent wave measurement surface in order to amplifyan evanescent wave measurement signal from membranes of the plurality ofred blood cells.
 67. An apparatus, comprising: a container; and anoptical waveguide comprising an evanescent wave measurement surface on alower end portion of a container, the container comprising a solution,the solution comprising a density in order to settle red blood cellswith gravimetric separation onto the evanescent wave measurementsurface.
 68. An apparatus as in claim 67, wherein the evanescent wavemeasurement surface is dimensioned for a plurality of red blood cellshaving a long dimension to be aligned with the evanescent wavemeasurement surface in order to amplify an evanescent wave measurementsignal from membranes of the plurality of red blood cells.
 69. Anapparatus to identify high blood pressure of a subject, comprising: aprocessor comprising instructions to identify the high blood pressure inresponse to spectra of a blood sample of the subject.
 70. An apparatusas in claim 69, wherein the processor comprises instructions todetermine a plurality of factors of the spectra and identify the highblood pressure in response to the plurality of factors of the spectra.71. An apparatus as in claim 69, wherein the spectra comprise avibrational spectra comprising one or more of infrared spectroscopy,near infrared spectroscopy, visible spectroscopy, Raman spectroscopy,nuclear magnetic resonance spectroscopy, total internal reflectionspectroscopy, total internal reflection infrared spectroscopy,transmission spectroscopy, transmission infrared spectroscopy ortransmission near infrared spectroscopy.
 72. An apparatus as in claim69, wherein the processor comprises instructions to determine thespectra in response to one or more of drying of the blood sample,washing of the blood sample, hyper-molality of the blood sample,hypo-molality of the blood sample, temperature of the blood sample,heating of the blood sample, cooling of the blood sample, pressure ofthe blood sample, pressurization of the blood sample, depressurizationof the blood sample.
 73. An apparatus as in claim 69, wherein theprocessor comprises instructions to identify the high blood pressure inresponse to a time series of the spectra as water is removed from thesample.
 74. An apparatus as in claim 69, wherein the processor comprisesinstructions to identify the high blood pressure with about 50% of thewater of the blood sample removed to inhibit a water signal of the bloodsample.
 75. An apparatus as in claim 69, wherein the processor comprisesinstructions to identify the high blood pressure of the blood samplewith the water at least partially removed from the blood sample toinhibit a water signal of the sample and inhibit lysing of the red bloodcells, the processor comprising instructions to measure the blood samplewith the red blood cell membranes substantially intact and adsorbed on ameasurement surface with the water at least partially removed from thesample.
 76. An apparatus as in claim 69, wherein the processor comprisesinstructions to identify the high blood pressure in response a pluralityof spectra of the blood sample, each of the plurality of spectracorresponding to a different osmolality of the blood sample.
 77. Anapparatus to identify malaria of a subject, comprising: a processorcomprising instructions to identify the malaria in response to spectraof a blood sample of the subject.
 78. An apparatus as in claim 77,wherein the processor comprises instructions to identify malaria priorto formation of a ring structure of the blood sample indicative ofmalaria visible with a microscope.
 79. A method of treating a patient,the method comprising: identifying a condition of the patient inresponse to spectra of a blood sample of the patient.
 80. A method oftreating a patient, the method comprising: identifying a condition ofthe patient in response to spectra of a blood sample of the patient;and. treating the patient with an amount of a drug in response to thecondition identified with the spectral signal.
 81. A method of treatinga patient, the method comprising: identifying a drug to be provided tothe patient in response to spectra of a blood sample of the patient; andproviding an amount of the drug in response to subsequent spectra ofsubsequent blood samples of the patient.
 82. A method as in claim 81wherein the amount of drug has been validated with one or more clinicaltrials to treat a condition in response to the spectra.
 83. A method ofperforming a clinical trial to evaluate one or more of a safety or anefficacy of a treatment with one or more of a device or a drug, themethod comprising: providing a spectrometer as in any one of thepreceding claims and measuring blood samples of one or more patientswith the spectrometer.
 84. An apparatus to treat a patient, theapparatus comprising: a spectrometer to measure spectra of a bloodsample of the patient; and an adjunct drug to treat the patient; whereinthe spectrometer comprises instructions to determine an amount of theadjunct drug to provide to the patient in response to the spectra of theblood sample of the patient.
 85. A method, the method comprising usingthe apparatus as in any one of the preceding claims.
 86. A method orapparatus as in any one of the preceding claims, wherein the markercomprises a plaque biomarker.
 87. A method or apparatus as in any one ofthe preceding claims, wherein the plaque biomarker comprises a materialof one or more of a foam-cell rich plaque, a lipid-rich plaque, or acollagen-rich plaque.
 88. A method or apparatus as in any one of thepreceding claims, wherein the marker comprises a blood pressurebiomarker.
 89. A method or apparatus as in any one of the precedingclaims, wherein the processor comprises instructions to identify thehigh blood pressure biomarker in response to an evanescent wavemeasurement of the sample on a measurement surface.
 90. A method orapparatus as in any one of the preceding claims, wherein the processorcomprises instructions to identify the high blood pressure biomarker inresponse to a transmission measurement of the sample between themeasurement surface and an opposing surface.
 91. A method or apparatusas in any one of the preceding claims, wherein the processor isconfigured to identify the biomarker in response to the blood samplecomprising one or more of red blood cells, serum or plasma and whereinthe apparatus is configured to measure a first component of the samplecomprising an increased amount of red blood cells in relation to thesample and a second component of the sample comprising a decreasedamount of red blood cells in relation to the sample.
 92. A method orapparatus as in any one of the preceding claims, wherein the processoris configured to measure the first component of the blood sample withone or more of a first evanescent wave measurement or a first opticaltransmission measurement and to measure the second component with one ormore of second evanescent wave measurement or a second opticaltransmission measurement.
 93. A method or apparatus as in any one of thepreceding claims, wherein the processor is configured to measure thefirst component of the blood sample with the first evanescent wavemeasurement and the first optical transmission measurement and tomeasure the second component with the second evanescent wave measurementand the second optical transmission measurement.
 94. A method orapparatus as in any one of the preceding claims, further comprising: anoptically transmissive material having a measurement surface to receivethe blood sample; one or more optical energy detectors coupled to theprocessor and optically coupled to the surface to measure an amount ofthe biomarker on the measurement surface.
 95. A method or apparatus asin any one of the preceding claims, further comprising: one or morelight sources to generate one or more measurement light beams; one ormore optical elements arranged to direct the one or more measurementlight beams from the one or more light sources to the surface and todirect light from the measurement surface to the one or more opticalenergy detectors; wherein the one or more optical elements is configuredto measure a surface of one or more red blood cells on the measurementsurface with an evanescent wave extending from the measurement surfaceto the one or more red blood cells on the measurement surface andwherein the biomarker comprises a biomarker of a membrane of a red bloodcell.
 96. A method or apparatus as in any one of the preceding claims,wherein the apparatus is configured to measure the one or more red bloodcells in the presence of an anticoagulant in order to permit a pluralityof red blood cells to contact the surface with each of said plurality ofred blood cells having an elongate surface extending along an elongatedimension aligned with the surface in order to measure a membrane ofsaid each of the plurality of red blood cells.
 97. A method or apparatusas in any one of the preceding claims, further comprising: a prismcomprising the optically transmissive material, the prism comprising themeasurement surface to receive the blood sample.
 98. A method orapparatus as in any one of the preceding claims, wherein the prismcomprises a Dove prism and wherein the Dove prism is illuminated at afirst angle with a first light beam to measure one or more red bloodcells on the measurement surface with the evanescent wave of lightinternally reflected from the measurement surface and wherein the Doveprism is illuminated with a second light beam at a second angle tomeasure light transmitted through the measurement surface and the bloodsample.
 99. A method or apparatus as in any one of the preceding claims,wherein the Dove prism comprises an elongate axis and a width transverseto the elongate axis and wherein the axis and the measurement surfaceextend in a first direction and the width extends in a second directiontransverse to the first direction.
 100. A method or apparatus as in anyone of the preceding claims, wherein the Dove prism comprises a firstinclined surface extending at a first angle oblique to the measurementsurface, and a second inclined surface extending a second angle obliqueto the measurement surface and wherein the light beam illuminates thesurface with total internal reflection to provide the evanescent waveand the light from the evanescent wave is transmitted through the secondinclined surface.
 101. A method or apparatus as in any one of thepreceding claims, wherein the measurement surface is located between thefirst inclined surface and the second inclined surface and wherein anopposing surface extends between the first inclined surface and thesecond inclined surface opposite the measurement surface and wherein atransmission measurement light beam is transmitted through the opposingsurface and the measurement surface to measure transmission through thesample.
 102. A method or apparatus as in any one of the precedingclaims, the processor comprises instructions pressurize the sample andto identify the blood pressure biomarker based on one or more temporaldenaturation profiles in response to pressurization of a sample chamberand wherein the optically transmissive substrate is dimensioned topressurize the blood sample.
 103. A method or apparatus as in any one ofthe preceding claims, wherein the one or more light sources and the oneor more detectors and the optics are configured to measure chemicalsbased on optical spectroscopy that are correlated with presence ofhypertension and/or correlated with level of blood pressure.
 104. Amethod or apparatus as in any one of the preceding claims, wherein theprocessor comprises one or more instructions to one or more of transformor calibrate biomarker concentration data into a corresponding scalerelated a systolic and a diastolic pressure during the cardiac cycle,such that the biomarker concentration is transformed from aconcentration to a blood pressure in response to measured data of aplurality of subjects.
 105. A method or apparatus as in any one of thepreceding claims, wherein the blood pressure biomarker comprises one ormore of adenosine triphosphate, or, one or more transmembrane proteins,one or more proteins of the membrane skeleton, one or more lipids of thered blood cell membrane, a relative ratio of the one or more lipids ofthe red blood cell membrane, or biomaterial deposited on the surface ofthe red blood cell membrane.
 106. A method or apparatus as in any one ofthe preceding claims, wherein the biomarker comprises a spectral signaland wherein the instructions comprise instructions to identify thebiomarker in response to the spectral signal.
 107. A method or apparatusas in any one of the preceding claims, wherein the biomarker comprises aplurality of biomarkers comprising a spectral signal and wherein theinstructions comprise instructions to identify the plurality ofbiomarkers in response to the spectral signal.
 108. A method orapparatus as in any one of the preceding claims, wherein the biomarkercomprises one or more of a protein, a lipid, a high density lipoprotein,a low density lipoprotein, membrane protein, a trans membrane protein, alipid, or a spectrin network and wherein spectra of the biomarkercomprise one or more of an Amide I peak, an Amide II peak, a Carboxylatepeak, or an Amide III band.
 109. A method or apparatus as in any one ofthe preceding claims, and wherein the Amide I peak, the Amide II peak,and the Amide III band correspond to an alpha helix of the biomarker, abeta sheet of the biomarker, and disordered protein of the biomarker,respectively.
 110. A method or apparatus as in any one of the precedingclaims, wherein the instructions comprise instructions to identify thebiomarker in response to one or more of the Amide I peak, the Amide IIpeak, the Carboxylate peak, or the Amide III band.
 111. A method orapparatus as in any one of the preceding claims, wherein the biomarkercomprises a spectral signal and wherein instructions compriseinstructions to determine a plurality of factors in response to thespectral signal.
 112. A method or apparatus as in any one of thepreceding claims, wherein the plurality of factors comprises a pluralityof functions.
 113. A method or apparatus as in any one of the precedingclaims, wherein the plurality of factors comprises a plurality offactors of a neural network.
 114. A method or apparatus as in any one ofthe preceding claims, wherein the plurality of factors comprises aplurality of at least four factors to identify the biomarker.
 115. Amethod or apparatus as in any one of the preceding claims, wherein theplurality of factors comprises a plurality of at least ten factors toidentify the biomarker.
 116. A method or apparatus as in any one of thepreceding claims, wherein the plurality of factors comprises a pluralityof at least ten orthogonal factors to identify the biomarker.
 117. Amethod or apparatus as in any one of the preceding claims, wherein theplurality of factors comprises a plurality of multivariate curveresolution factors or a plurality of multi-component analysis factors.118. A method or apparatus as in any one of the preceding claims,wherein the instructions comprise instructions to identify the biomarkerin response to a relationship among a plurality of factors determined inresponse to the spectral signal.
 119. A method or apparatus as in anyone of the preceding claims, wherein the biomarker comprises a red bloodcell membrane marker responsive to aspirin.
 120. A method or apparatusas in any one of the preceding claims, wherein the biomarker comprises ared blood cell membrane marker responsive to gluteraldehyde.
 121. Amethod or apparatus as in any one of the preceding claims, comprising: aprocessor comprising instructions to identify the biomarker of a thecell membrane of the subject.
 122. A method or apparatus as in any oneof the preceding claims, comprising: identifying a biomarker of a cellmembrane of the subject.
 123. A method or apparatus as in any one of thepreceding claims, comprising: identifying a blood pressure biomarker ofa blood sample of the subject.
 124. A method or apparatus as in any oneof the preceding claims, wherein the biomarker has been correlated withhigh blood pressure in order to quantify the high blood pressure.
 125. Amethod or apparatus as in any one of the preceding claims, furthercomprising using the apparatus as in any one of the preceding claims.126. A method or apparatus as in any one of the preceding claims,comprising, measuring a spectrum of one or more red blood cells; anddetermining a risk of cardiovascular disease in response to thespectrum.
 127. A method or apparatus as in any one of the precedingclaims, wherein the spectrum comprises an evanescent wave spectrum. 128.A method or apparatus as in any one of the preceding claims, wherein therisk is determined without in vitro enzymatic analysis.
 129. A method orapparatus as in any one of the preceding claims, wherein the risk isdetermined without lysing the red blood cells.
 130. A method orapparatus as in any one of the preceding claims, wherein the risk isdetermined without or pretreating a sample comprising the one or morered blood cells.
 131. A method or apparatus as in any one of thepreceding claims, wherein the spectrum comprises a plurality of spectra.132. A method or apparatus as in any one of the preceding claims,comprising: a sample container to receive a blood sample of the subject;and a spectrometer comprising one or more optics to couple to thecontainer.
 133. A method or apparatus as in any one of the precedingclaims, wherein the sample container extends in a vertical direction adistance sufficient to gravimetrically separate red blood cells fromserum of the blood sample.
 134. A method or apparatus as in any one ofthe preceding claims, wherein the sample container extends in a verticaldirection a distance sufficient to wash the red blood cells when the redblood cells separate from the serum and wherein the container comprisesa volume of a solution sufficient to wash the red blood cells.
 135. Amethod or apparatus as in any one of the preceding claims, wherein thesample container comprises a solution having a density less than adensity of red blood cells in order to separate gravimetrically separatethe red blood cells from the plasma.
 136. A method or apparatus as inany one of the preceding claims, wherein the solution comprises aspectroscopic reference marker in order to one or more of calibrate thespectrometer or identify spectral shifts in relation to the referencemarker.
 137. A method or apparatus as in any one of the precedingclaims, wherein the sample container comprises a column extending in avertical direction.
 138. A method or apparatus as in any one of thepreceding claims, wherein the sample container comprises a removablecontainer comprising a waveguide to introduce an evanescent wave into alower portion of the container.
 139. A method or apparatus as in any oneof the preceding claims, wherein the waveguide comprises Germanium. 140.A method or apparatus as in any one of the preceding claims, wherein thewaveguide comprises a lower end of the container to support the solutionand receive red blood cells near an upper surface of the waveguide. 141.A method or apparatus as in any one of the preceding claims, wherein anupper surface of the waveguide is configured to contact the solution andreceive a red blood cell on the upper surface.
 142. A method orapparatus as in any one of the preceding claims, wherein an uppersurface of the waveguide and the lower surface of the container arelocated within a 1/e intensity distance of the evanescent wave in orderto measure red blood cells on the lower surface of the container.
 143. Amethod or apparatus as in any one of the preceding claims, wherein thespectrometer comprises coupling optics and a support fixed in relationto the coupling optics, the support configured to support the removablecontainer, wherein the support and the coupling optics are arranged toalign the waveguide with the coupling optics when the container issupported with the support.
 144. A method or apparatus as in any one ofthe preceding claims, wherein the coupling optics comprise an inputoptic to direct light into the waveguide and an output optic to receivelight from the waveguide and wherein the support is fixed to thespectrometer and sized and shaped in order to align the input optic witha first end of the waveguide and the output optic with a second end ofthe waveguide when the container rests on the support.
 145. A method orapparatus as in any one of the preceding claims, wherein the samplecontainer comprises a removable sample container having membrane on abottom of the container and wherein the waveguide comprises an uppersurface to support the membrane and wherein the membrane comprises athickness of no more than a 1/e distance of the evanescent wave from thewaveguide in order to measure red blood cells through the membrane withthe evanescent wave.
 146. A method or apparatus as in any one of thepreceding claims, wherein the waveguide comprises one or more prisms.147. A method or apparatus as in any one of the preceding claims,wherein the waveguide comprises a first flat surface on a first side anda second flat surface on a second side opposite the first side and afirst optically coupling end to transmit light between the first flatsurface and the second flat surface and a second end optically couplingend to transmit light reflected between the first flat surface and thesecond flat surface to a detector.
 148. A method or apparatus as in anyone of the preceding claims, wherein the waveguide comprises thicknessextending between the first surface and a second surface a lengthextending in a direction of propagation of the light energy between thefirst end and the second end and a width transverse to the length, thewidth greater than the thickness and the length greater than thethickness such that the light energy reflects a plurality of times fromthe first surface in order to increase a signal from the biomarker. 149.A method or apparatus as in any one of the preceding claims, comprising:providing an evanescent wave measurement surface on a lower end portionof a container, the container comprising a solution; placing a bloodsample in the solution, wherein red blood cells of the sample settlewith gravimetric separation onto the evanescent wave measurementsurface; and measuring spectra of the red blood cells on the evanescentwave measurement surface.
 150. A method or apparatus as in any one ofthe preceding claims, wherein the red blood cells comprise a pluralityof red blood cells having a long dimension aligned with the evanescentwave measurement surface in order to amplify an evanescent wavemeasurement signal from membranes of the plurality of red blood cells.151. A method or apparatus as in any one of the preceding claims,comprising: a container; and an optical waveguide comprising anevanescent wave measurement surface on a lower end portion of acontainer, the container comprising a solution, the solution comprisinga density in order to settle red blood cells with gravimetric separationonto the evanescent wave measurement surface.
 152. A method or apparatusas in any one of the preceding claims, wherein the evanescent wavemeasurement surface is dimensioned for a plurality of red blood cellshaving a long dimension to be aligned with the evanescent wavemeasurement surface in order to amplify an evanescent wave measurementsignal from membranes of the plurality of red blood cells.
 153. A methodor apparatus as in any one of the preceding claims, comprising: aprocessor comprising instructions to identify the high blood pressure inresponse to spectra of a blood sample of the subject.
 154. A method orapparatus as in any one of the preceding claims, wherein the processorcomprises instructions to determine a plurality of factors of thespectra and identify the high blood pressure in response to theplurality of factors of the spectra.
 155. A method or apparatus as inany one of the preceding claims, wherein the spectra comprise avibrational spectra comprising one or more of infrared spectroscopy,near infrared spectroscopy, visible spectroscopy, Raman spectroscopy,nuclear magnetic resonance spectroscopy, total internal reflectionspectroscopy, total internal reflection infrared spectroscopy,transmission spectroscopy, transmission infrared spectroscopy ortransmission near infrared spectroscopy.
 156. A method or apparatus asin any one of the preceding claims, wherein the processor comprisesinstructions to determine the spectra in response to one or more ofdrying of the blood sample, washing of the blood sample, hyper-molalityof the blood sample, hypo-molality of the blood sample, temperature ofthe blood sample, heating of the blood sample, cooling of the bloodsample, pressure of the blood sample, pressurization of the bloodsample, depressurization of the blood sample.
 157. A method or apparatusas in any one of the preceding claims, wherein the processor comprisesinstructions to identify the high blood pressure in response to a timeseries of the spectra as water is removed from the sample.
 158. A methodor apparatus as in any one of the preceding claims, wherein theprocessor comprises instructions to identify the high blood pressurewith about 50% of the water of the blood sample removed to inhibit awater signal of the blood sample.
 159. A method or apparatus as in anyone of the preceding claims, wherein the processor comprisesinstructions to identify the high blood pressure of the blood samplewith the water at least partially removed from the blood sample toinhibit a water signal of the sample and inhibit lysing of the red bloodcells, the processor comprising instructions to measure the blood samplewith the red blood cell membranes substantially intact and adsorbed on ameasurement surface with the water at least partially removed from thesample.
 160. A method or apparatus as in any one of the precedingclaims, wherein the processor comprises instructions to identify thehigh blood pressure in response a plurality of spectra of the bloodsample, each of the plurality of spectra corresponding to a differentosmolality of the blood sample.
 161. A method or apparatus as in any oneof the preceding claims, comprising: a processor comprising instructionsto identify the malaria in response to spectra of a blood sample of thesubject.
 162. A method or apparatus as in any one of the precedingclaims, wherein the processor comprises instructions to identify malariaprior to formation of a ring structure of the blood sample indicative ofmalaria visible with a microscope.
 163. A method or apparatus as in anyone of the preceding claims, comprising: identifying a condition of thepatient in response to spectra of a blood sample of the patient.
 164. Amethod or apparatus as in any one of the preceding claims, comprising:identifying a condition of the patient in response to spectra of a bloodsample of the patient; and treating the patient with an amount of a drugin response to the condition identified with the spectral signal.
 165. Amethod or apparatus as in any one of the preceding claims, comprising:identifying a drug to be provided to the patient in response to spectraof a blood sample of the patient; and providing an amount of the drug inresponse to subsequent spectra of subsequent blood samples of thepatient.
 166. A method or apparatus as in any one of the precedingclaims, wherein the amount of drug has been validated with one or moreclinical trials to treat a condition in response to the spectra.
 167. Amethod or apparatus as in any one of the preceding claims, comprising:providing a spectrometer as in any one of the preceding claims andmeasuring blood samples of one or more patients with the spectrometer.168. A method or apparatus as in any one of the preceding claims,comprising: a spectrometer to measure spectra of a blood sample of thepatient; and an adjunct drug to treat the patient; wherein thespectrometer comprises instructions to determine an amount of theadjunct drug to provide to the patient in response to the spectra of theblood sample of the patient.
 169. A method or apparatus as in any one ofthe preceding claims, further comprising identifying a transition metalcarbonyl band.
 170. A method or apparatus as in any one of the precedingclaims, further comprising identifying a transition metal carbonyl bandin a pure component spectrum of blood to build a calibration model. 171.A method or apparatus as in any one of the preceding claims, furthercomprising building a calibration model to predict blood pressure from aspectrum of red blood cells.
 172. A method or apparatus as in any one ofthe preceding claims, further comprising building a calibration model topredict blood pressure from a spectrum of red blood cells, based on oneor more spectral signals corresponding to one or more biomarkers.
 173. Amethod or apparatus as in any one of the preceding claims, whereinbuilding a calibration model to predict blood pressure comprisesgenerating a pure component spectrum of red blood cells from a spectrumof red blood cells.
 174. A method or apparatus as in any one of thepreceding claims, wherein the pure component spectrum of red blood cellscomprises one or more spectral signals corresponding to one or morebiomarkers of blood pressure.
 175. A method or apparatus as in any oneof the preceding claims, further comprising building a calibration modelto predict blood pressure from a spectrum of red blood cells, based oneor more spectral signals comprising an amide band, a carboxylate band, atransition metal carbonyl band, or a combination thereof.
 176. A methodor apparatus as in any one of the preceding claims, wherein a transitionmetal carbonyl band corresponds to a transition metal carbonyl bondresulting from a hemoglobin-spectrin complex.
 177. A method or apparatusas in any one of the preceding claims, wherein a selection ofwavelengths to be used in sample spectral analysis is optimized using agenetic algorithm.
 178. A method or apparatus as in any one of thepreceding claims, wherein a selection of blood pressure measurements tobe used as reference values for blood spectral analysis is optimizedusing a genetic algorithm.
 179. A method or apparatus as in any one ofthe preceding claims, wherein a selection of time points of a samplepreparation process to be used in sample spectral analysis is optimizedusing a genetic algorithm.
 180. A method or apparatus as in any one ofthe preceding claims, wherein a selection of time points of a bloodsample drying process to be used in blood spectral analysis is optimizedusing a genetic algorithm.